Development of Structural Damage Inspection and Maintenance System Based on Mixed Reality
Though automatic structural damage inspection methods based on mobile robots have been rapidly developed recently, they are not able to completely replaced conventional human based inspections due to limitations such as the false alarm of automatic damage detection techniques. In this study, a human centered inspection system with aid of an augmented reality framework is developed in order to improve convenience in inspecting and managing various types of structural damages such as spalling, exposed rebars and efflorescence. The developed system automatically detects and quantifies structural damages, and displays the inspection results in real-time through an augmented reality device. In addition, the previously detected damages are visualized with holographic markers and their information at the exact location. Therefore, an inspector can easily find where the previous damages were and whether the damages become severe or not. The performance of the developed system was validated through a field test and it was revealed that the system can save inspection time and improve convenience by accelerating essential tasks of the inspector such as damage detection, size measurement and finding locations of previous damages and determining whether the damages become severe or not.
Structural Damage Detection, Localization, and Quantification via UAV-Based 3D Imaging
Visual damage inspection for civil structures is a labor-intensive and timeconsuming task. We propose an autonomous UAV-based pipeline for crack and spalling detection, localization, and quantification. Through fusing 3-dimensional (3D) reconstruction and 2D damage detection after performing UAV-based imaging for an engineering structure, the process generates a damage-annotated 3D information model with rich metadata, including the size and type of damage and its location relative to the structure. The pipeline is composed of four steps: image acquisition via UAV, 3D scene reconstruction, crack/spalling detection and extraction using a deep neural network, and 3D damage localization and quantification. To validate this process, UAV images from three full-scale concrete columns are processed, and results are evaluated in this paper. The results demonstrate that the proposed pipeline can provide accurate and informative 3D condition mapping for civil structures. The authors envision that by employing this UAV-based automatic process, structural damage inspection can be conducted much frequently and rapidly with a significantly low cost.
Techniques for Contact-Based Structural Health Monitoring with Multirotor Unmanned Aerial Vehicles
Use of Unmanned Aerial Vehicles (UAVs) for Structural Health Monitoring (SHM) has become commonplace across civil and energy generation applications with hazardous or time-consuming inspection processes. Expanding upon surface screening offered by non-contact remote visual inspection UAVs, systems are now beginning to incorporate contact-based Non-Destructive Evaluation (NDE) transducers to detect and monitor incipient sub-surface flaws. However, challenges to environmental interaction using conventional multirotor platform dynamics amid aerodynamic disturbances have frustrated efforts for stable and repeatable sensor placement. Herein, two distinct UAV systems are evaluated as a means to overcome these challenges. The first utilizes vectored thrust with a tri-copter layout. It may dynamically reorient dual-axis tilting propellers to directly effect interaction force and deploy drycoupled ultrasonic thickness measurement across omnidirectional targets. In static point and rolling scan measurement, laboratory tests demonstrate mean absolute error below 0.1 mm and 0.3 mm, respectively. The second UAV uses rigidly affixed multidirectional propellers to reverse and redirect its net thrust. Landing atop cylindrical structures it may crawl around their circumference, supporting itself without magnetic or vacuum adhesion. Arbitrary static position is maintained to within a mean deviation of 0.7 mm. Lastly, comparative discussion of each system informs strategies for further development of contact-based aerial SHM and its adoption to industrial practice.
On the Challenges of Upscaling Damage Monitoring Methodologies for Stiffened Composite Aircraft Panels
Health management methodologies for condition-based maintenance are often developed using sensor data collected during experimental tests. Most tests performed in laboratories focus on a coupon level or flat panels, while structural component testing is less commonly seen. As researchers, we often consider our experimental tests to be representative of a structure in a final application and consider the developed methodologies to be transferrable to these real-life structures. Yet, structures in their final applications such as wind turbines or aircraft are often larger, more complex, might contain various assembly details, and are loaded in complex conditions. These factors might influence the performance of developed diagnostic and prognostic methodologies and should therefore not be ignored. In our work, we consider the aspects of upscaling structural health monitoring (SHM) methodologies for stiffened composite panels with the design of the panels inspired by an aircraft wing structure. For this, we examine two levels of panels, namely a single- and multi-stiffener composite panel, where we consider the single-stiffener panel to be a representative lower-level version of the multi-stiffener panel. Multiple SHM sensors (acoustic emission, Lamb waves, strain sensing) were installed on both composite panels to monitor damage propagation during testing. We identify and analyse challenges and further discuss considerations that must be taken during upscaling of diagnostics and prognostics, and with that, aid in the development of health management methodologies for condition-based maintenance.
Retrofitting Potentials in Aircraft Structural Health Monitoring A Value of Information Analysis
Structural Health Monitoring (SHM) systems promise to improve cost efficiency in aircraft maintenance. Beyond the cost of developing and procuring SHM systems, however, a potentially adverse impact on aircraft performance may negatively affect operational cost. With this in mind, we use an SHM sensor-network model to derive optimal SHM configurations, considering instrumentation and fuel costs as well as saved inspection time, on individual structural component level. Based on Net Present Value theory, we find that retrofitting provides a 20-% benefit on fleet level over factory-only instrumentation, considering the increasing maintenance effort throughout aircraft life as well as variations in individual aircraft usage. We also show that a Value of Information analysis supports more gainful decisions regarding the optimal set of instrumented parts as well as retrofitting times, considering individual aircraft usage.
Cost and Benefit of SHM in Commercial Aviation
An integrated techno-economic cost-benefit analysis is presented to analyze the impact of damage detection-based SHM on an Airbus A320-based reference aircraft, instrumented with ultrasonic and fiber-optic sensors. The operational performance of aircraft with and without SHM is compared in terms of inspection effort, dispatch reliability, payload capacity, service limit, SHM equipment weight and performance, as well as total operating cost. Finally, the net present value of SHM is calculated. While SHM can be profitable for airlines, the achievable benefit depends on the SHM system performance and the economic environment of the airline.
Damage Localization and Magnitude Estimation on a Composite UAV Wing via Stochastic Functionally Pooled Models
A vibration-based active-sensing global SHM method is proposed and evaluated for its damage localization and quantification accuracy on complex wing structure. In the process, the wing structure is actuated by a white noise vibration and the response signals are collected by a distributed sensor network. The proposed SHM method first utilize auto-regressive exogenous (ARX) model [1] for representing the time-domain response at each sensor location under various damage conditions, where stochasticity contained in structural response is minimized and identified. ARX models are then mapped to damage parameter space via vector-dependent functionally pooled (VFP) method [2]. Then, a damage estimation algorithm based on minimizing VFP-ARX model prediction error is developed. Finally, the damage estimation results are evaluated as the possibility of leveraging multiple senor signal in SHM process is implicated.
Structural Health Monitoring in Open-Hole CFRP Laminates Under Fatigue Loading Based on Thermodynamic Entropy Analysis
Structural health monitoring of composite laminates has been studied in the past decades. Many empirical and analytical approaches have been proposed in the literature. Recently, thermodynamic entropy-based techniques have gained more attention, due to the potential for removing the dependency of the common approaches on experimental conditions to provide an independent measure of damage. This study applies the thermodynamic entropy concepts and measures the entropy generation due to damage in step-stress tensile fatigue loadings. Several tests are performed on open-hole CFRP laminates, and mechanical and temperature responses are recorded. This allows for the energy dissipation associated with thermal and mechanical mechanisms that contribute to entropy generation to be carefully isolated and measured. Furthermore, the damage associated with residual plastic deformation is measured on the surface using Digital Image Correlation (DIC). Experimental results indicate that although the trajectory of plastic deformation, internal damage evolution, and heat conduction energy dissipation drastically differ in different tests, the final entropy generation values at the time of fracture converge to a critical value, which is known as the entropy endurance limit or fatigue fracture entropy (FFE).
Vibration-Based Structural Health Monitoring of Delivery Drones: Analysis of Preliminary Experiments
The development of unmanned aerial vehicles (UAV) has progressed very rapidly in recent years and finds many applications in various industrial fields. The technological progress brings serious challenges, especially regarding flight safety, as any unexpected behavior of the drone can lead to serious consequences. The autonomous usage of delivery drones requires a high reliability especially in urban areas. This motivates the development and application of structural health monitoring (SHM) systems. In this work, we present and discuss the results of preliminary experiments of a vibration-based SHM system for delivery drones. In particular, we focus on the hover phase during take-off in which the airworthiness can be assessed within seconds. For this purpose, the drone is first launched and a measurement is made using acceleration sensors. The measurement data is evaluated using three different metrics one of which is the Nullspace-Based Fault Detection (NSFD) method. It was demonstrated here that added masses can be detected through the analysis of mechanical vibrations.
On a Population-Based Structural Health Monitoring Framework: An Aerospace Case Study
One of the biggest problems facing the uptake of structural health monitoring (SHM) in industry is a general lack of available damage-state data. Population-based structural health monitoring (PBSHM) is one solution to this problem, expanding the available information and datasets for a monitoring campaign by leveraging measurements across populations of structures and computer models. The information from across a population can be utilised in performing and improving inferences on particular target structures or the complete population as a whole. The challenge in PBSHM is in knowing which information should be shared between individual structures in a population, and how that information can be shared and transferred between members of the population. This paper demonstrates a methodology for performing PBSHM, which is experimentally validated on a population of aircraft wings, transferring label information from a Gnat trainer aircraft wing to an unlabelled Piper Tomahawk aircraft wing dataset. The proposed approach combines abstract representations of structures as graphs with transfer learning technologies, in order to produce a classifier that generalises across the population. The methodology is shown to be successful, with the labelled Gnat dataset diagnosing locations of damage on the (unlabelled) Piper Tomahawk aircraft with 100% accuracy.
Monitoring of Rail-Tracks Based on Measured Acceleration Data
Railway tracks are used as mass transportation system for transporting large number of people and goods from place-to-place to keep the economy running smoothly. Hence it is inevitable to keep the tracks healthy for safe and on-time movement of trains. Traintracks are complex systems that contain ballast, sleepers, fasteners and rails. Therefore, monitoring only one/two elements (e.g., ballast/train-track) will not provide enough information to understand the overall performance of the railway tracks. To tackle such issue, herein a sensor fusion i.e., accelerometers, fiber-optic sensors, strategy is adopted and sensors are placed at different locations of a real rail-track. In order to measure the vibration signal four accelerometers are employed, first one is placed on the rail (between two sleepers), second one is installed on the rail but above the sleeper, third one is exactly on the sleeper, and last one is on the precast railway trough. In a first step, the investigation has focused into accelerometers data only. The tests are performed for the following loading conditions: (i) shaking the track via an APS400 type shaker, (ii) hitting the track by an impact hammer, and (iii) by passing a real train on the track. The time-series data are analyzed and the frequencies and spectrums are estimated via the use of fast Fourier transform (FFT). The changes of frequencies of the tested rail-track at different locations due to the various loading conditions are observed. In a later step, an autoregressive type time-series model has been developed and validated where the initially obtained results show good agreement with the measured data. The current findings will assist to monitor the rail-track for any further changes.
A Business Intelligence Approach to Prioritize Bridge Maintenance Activities
Given the general condition of road infrastructure in Costa Rica, the proper prioritization of maintenance activities for bridges is essential for government institutions to effectively plan and assign resource investments. This work presents the main results of an extension project developed by the e-Bridge program of the Costa Rica Institute of Technology, with the objective of designing and applying a methodology for prioritizing maintenance activities for bridges, taking as a case study the actual bridges managed by a specific regional municipality. To this end, first, a given set of bridges were inspected and evaluated. Then, with this detailed inventory information, a set of key bridge performance indicators were defined including structural condition, environmental variables, and socio-economical categories. Consequently, a tailor-made methodology was proposed to prioritize different kinds of maintenance activities for the respective bridges using the above-mentioned indicators. The methodology was implemented using a business intelligence tool to manage all the information and solve prioritization queries. This tool and the major findings of the project were shared during the project with community actors and municipality collaborators through several workshops. The resulting methodology and developed tool effectively support decision-making regarding bridge maintenance activities for the target municipality and could be applied nation-wide.
Battery-Free Structural Health Monitoring System for Concrete Structures
This paper presents a cyber-physical system based on a wireless sensor network dedicated to structural health monitoring of reinforced concretes throughout their lifetime. This cyber-physical system is intended to implement a communicating reinforced concrete. Two types of nodes compose this WSN. The sensing node is fully wireless, can measure various parameters (such as temperature, relative humidity, mechanical strain, or resistivity), is battery-free, and is wirelessly and remotely powered and controlled via a radiative electromagnetic power transfer system by the second type of nodes, the communicating node. The communicating node connect the WSN to the digital world.
Battery-Free Bluetooth Low Energy Sensing Nodes for Structural Health Monitoring of Concretes
This paper presents a Bluetooth Low Energy sensing node, part of a wireless sensor network dedicated to the deployment of a cyber-physical system for the structural health monitoring of reinforced concretes throughout their life. This fully wireless sensing node is designed to measure temperature and relative humidity, and wirelessly transmit the collected data in its network, as well as to be energy autonomous. For that, it is battery-free, able to cold-start, and wirelessly and remotely powered -and controlledover several meters by communicating nodes (other part of the network, assuring the connection to the digital world) via a radiative electromagnetic power transfer system.
Research on Monitoring and Diagnosis Technology of Roller Coaster Track Stress Based on Fiber Bragg Grating
Roller coaster bears alternating load, and fatigue damage is one of the main ways of its failure, and the fatigue of roller coaster structure and the resulting safety problems are becoming more and more prominent. With the rapid construction of large amusement facilities and the increasingly complex forms of movement, safety accidents caused by their failure occur from time to time. In this paper, according to the practical needs of the roller coaster track structure operation safety, the key failure parts of the roller coaster track structure are analyzed, and the fiber Bragg grating strain monitoring technology is used for real-time online monitoring of the key failure parts; Based on the monitoring stress spectrum data, the statistical counting method and cumulative damage theory are used to diagnose and predict the damage situation of the monitoring points, and the cumulative damage degree and crack initiation life of each monitoring point are given, so as to provide support for enterprises to make maintenance plans, and make the original cumbersome and difficult data acquisition more reliable, safe and convenient, Improve the ability and level of roller coaster monitoring and management.
Safety Assessment Method for Vehicle Transportation of Hazardous Chemicals on Cross-Sea Bridges
With the rapid development of China's chemical industry in coastal areas, the transportation of hazardous chemicals has become increasingly busy. Due to the complexity of marine environment, there are a large number of safety hazards during the transportation of hazardous chemicals. Based on accident statistics, the main factors affecting the transportation safety of hazardous chemicals is analyzed, including strong wind and reduced adhesion coefficient caused by rain and snow. Further, a vehicle stability analysis model considering these factors is established to calculate the critical wind speed of sideslip. Finally, the speed of the hazardous chemical vehicle is used as the safety evaluation index, and the safety critical speed surface is given. This research has important reference value for ensuring the transportation safety of hazardous chemicals and the operation of cross-sea bridges.
Monitoring Early Cement Hydration with Coda Wave Interferometry
The inorganic chemical reaction of hydraulic binders, such as cement with water, is a complex mechanism and a vital part of every building process involving concrete. Dur ing this hydration reaction, the system develops strength due to the formation of various solid hydration phases over time. In the case of ordinary Portland cements (OPC), the main hydration phase is called C-S-H phase, and is responsible for the early- and long- term strength development of cement. Therefore, the formation of this phase determines the time for continuing construction work, e.g. removing formwork or concreting of the following structures. Under laboratory conditions and for relatively small samples, methods such as quantitative X-ray diffraction analysis are able to monitor the progress of hydration over time. However, it is more challenging to monitor the hydration of larger structures under non-laboratory conditions. In this study, we propose a sensitive and robust monitoring method to detect and quantify the formation of various hydration phases during the early hydration of OPC using Coda Wave Interferometry, based on the evaluation of ultrasonic waves with embedded piezoelectric sensors. We investigated the signal correlation and velocity perturbation of ultrasonic signals during the first 24 hours of OPC hydration. We identified periods during which these parameters varied significantly and periods during which the signals stabilized, in our case approximately 18 hours after initiation of the reaction. Simultaneously, we monitored the hydration of OPC using similar but smaller samples by means of heat flow calorimetry and a Vicat penetration test. We predicted the evolution of the main hydration phases mass by thermodynamic modeling and validated our results as well as the assignment of characteristic periods to the formation of specific hydration phases in the time domain. Our proposed method adds to the spectrum of ultrasound- based methods for hydration monitoring. Our approach aims at a more sophisticated prediction of the early-age strength development of cement and concrete, even under site conditions and for large structures.
Shear Tests of Reinforced Concrete Beams with the Influence of Initial Shear Cracks
Cracks in reinforced concrete (RC) structures can be detrimental if they grow beyond the limits, so it is important to include their influences in the structural health monitoring method. For example, the impact of shear cracks on the structural performance of RC beams remains unknown. To fully understand the impact of cracks, judging by crack patterns alone is insufficient. This study focuses on the initial shear cracks and investigates whether shear cracks in different locations reduce the load-bearing capacity of beams at different levels. Four sets of beams that exhibited different types of initial shear cracks were cast, including (i) reference beams with no cracks, (ii) beams with inclined cracks in the bending span, (iii) beams with shear cracks in the shear span, and (iv) beams with pre-defined notches in the shear span. The initial cracks were carefully documented, and all the specimens were tested in four-point bending tests. The test results of beams with initial cracks or notches were compared with reference beams. The comparison indicated that the initial cracks in the bending span had limited influence on the load-bearing capacity, while the cracks in the shear span significantly reduced the ultimate load of the beams. Also, the cracks in the shear span were likely to further propagate to become the dominant cracks.
A High-Volume Processing Framework for Human-Structure Interfaces in Smart Infrastructure
Monitoring the behavior and performance of engineered structures has become increasingly desirable due to the value such information offers for occupant safety and structural maintenance. Vibration data collected from accelerometers has proven to be an effective tool to perform this type of monitoring. While some monitoring activities can occur autonomously, it is often necessary for humans to interact with the data to discern the need for additional evaluation. In large structures or those with a dense sensor deployment, continuously collected vibration data can quickly grow to massive scales. Consequently, the evaluation of structural performance is often limited by the ability of a system to efficiently process and present large volumes of data. To overcome this challenge, this paper presents a framework to process, store, and visualize data using open-source distributed computing technologies. The framework utilizes a publish-subscribe messaging queue deployed across multiple partitions to consume data in parallel, improving the rate of ingestion. Ingested data is stored in a structured format using a NoSQL database that provides high availability, scalability, and performance. The stored data acts as the source for webbased visualization. This setup provides a high degree of adaptability, allowing meaningful visualizations to be implemented for various forms of smart infrastructure monitoring tasks. The capabilities of the resultant human-infrastructure interface are demonstrated using Goodwin Hall, a five-story building instrumented with 225 hard-wired accelerometers. This case study showcases visualizations that enable users to perform real-time assessment of frequency domain features and efficiently identify notable excitation events during the building's history.
Deep Learning Framework for Post-Hazard Condition Monitoring of Nuclear Safety Systems
A novel approach is presented to conduct data-driven condition assessment in nuclear safety systems with the aid of deep learning. With the resurgence of nuclear energy due to the ever-increasing demand for electricity and carbon free power generation, ensuring safe operations at nuclear facilities is important. Nuclear safety systems, such as equipment-piping, undergo aging and subsequent degradation due to flow-accelerated erosion and corrosion. Conventional non-destructive techniques implemented during plant outages can take weeks and months to scan all the systems in their entirety. Continuous condition monitoring of such systems would result in lowering the maintenance costs along with extending the operating lifetime for a nuclear power plant. Additionally, the proposed framework should be able to detect minor degradation caused due to aging of nuclear facilities. Uncertainty in the degradation severity levels is also incorporated in the design of the condition assessment methodology. In this paper, the use of artificial intelligence (AI) algorithms as well as vibration-based health monitoring for degradation detection has been demonstrated. A simple equipment-piping system subjected to an external hazard, such as an earthquake, is selected as an application case study. A proof-of-concept is presented wherein the proposed framework utilizes the data collected from sensors to generate a machine learning data repository, demonstrates pattern recognition and feature extraction, explores the design of an artificial neural network (ANN), and develops a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop reactor plant. It is shown that the proposed post-hazard condition monitoring framework is able to detect degraded locations along with the severity levels with high degree of accuracy.
Intelligent Identification of Rivet Corrosion on Steel Truss Bridge by Single-Stage Detection Network
Rivet corrosion, which is a common disease of steel truss bridges, directly reflects the safety status of steel structures. The identification of rivet corrosion is critical to ensure the normal service of steel truss bridges. In practical engineering, the main detection method of rivet corrosion is manual visual inspection. However, this method has low efficiency and poses a threat to the personal safety. To address this issue, an intelligent identification method for rivet corrosion on steel truss bridges by a single shot detector (SSD) is proposed after obtaining the panoramic image of the bridge. The sub-images cut from the panoramic image are as the networks input. Considering the small size of bridge rivets and low precision of small object detection of SSD, this study divides the panoramic image into sub-images of 100 — 100 pixels, and then uses bilinear interpolation to resize the sub-images into 300 — 300 pixels. To improve the robustness of the detection model, gaussian noise, random rotation and roll-over tricks are applied to the original dataset. The expanded dataset includes 600 labelling images, which is divided into training set (80%) and testing set (20%), including corroded rivets and normal rivets. The network is trained with transfer learning technique for 12000 iterations, with cross entropy loss for classification and smooth L1 loss for location. The confidence threshold in network inference is set to 0.6 considering the rivet space distribution to reduce false positives of corroded rivets. The qualitative and quantitative testing results show the accuracy of the proposed approach.
Optimized Placement for Embedded Radar and Communication Sensors in Wind Turbine Blades
The present work describes the simulation procedure to determine an optimal sensor placement for RadCom (radar and communication) sensors operating in the frequency band from 57-63 GHz inside a wind turbine blade. Optimal placement means a full penetration and coverage of the blade as well as a communication path from every node to the blade's root can be achieved. Furthermore, triple coverage is necessary to allow the localization of structural changes in the blade and its surface, such as ice aggretion. The sensors are partly applied to the surface and partly embedded in the core material of the rotor blade. In this way the blade can be monitored during the entire operation for structural health monitoring (SHM) purposes. The simulations take into account the transmission of waves, refraction, dispersion in the material and are based on material data obtained from measurements of rotor blade materials, as well as antenna data. The resulting sensor distribution is the basis for a prototype design of a 30 m long blade with embedded sensors for full-scale SHM testing. Since embedded sensors are not accessible after completion of the manufacturing process, the simulation results are key to the experiments success.
Bayesian Model Updating for a Cable-Stayed Pedestrian Bridge Using DREAM and Kriging Model
Modeling error and measurement noise are inevitable and lead to a significant discrepancy between Finite element model (FEM) and a real structure. Finite element model updating (FEMU) is, therefore, necessary to match the measured data with a predicted response from FEM for advancing structural health monitoring (SHM). Bayesian approach has been proposed to identify the most probable values (MPVs) of physical parameters and provide parameters uncertainties. However, the current Bayesian approach has challenges in high-dimensional problems and requires high computational costs in the complex structure. In this study, a new Bayesian updating framework is proposed using Differential Evolution Adaptive Metropolis (DREAM) sampling method with a variance-based global sensitivity analysis (GSA) and Kriging model to enhance the Bayesian approachs performance and computational efficiency. Firstly, variance-based GSA is used to eliminate insignificant parameters to measured responses and reduce model dimensionality. Secondly, a Kriging model is employed as a surrogate of the time-consuming FE model for reducing the computational burden. DREAM is essentially a multi-chain sampling method, which parallelly runs different paths for all possible solutions and accurately approximates the posterior distribution density function (PDF) for the Bayesian approach. The demonstration of the proposed updating framework of a real-world cable-stayed pedestrian bridge is presented.
Damage Detection on Offshore Wind Turbine Jacket Foundations Based on an AutoEncoder
This work addresses the problem of damage detection on offshore wind turbine jacket-type foundations based on deep learning algorithms. The work utilizes data obtained from the vibration response of a lab-scale wind turbine foundation. The main contributions of this manuscript to damage detection are: (i) an autoencoder neural network trained with only healthy data drawing a normality model, and (ii) a threshold in the function of prediction errors to define the bound limits of damage. The methodology is evaluated using real vibration data from the lab-scale wind turbine foundation tagged with different noise levels and damage scenarios. The results of damage detection show a 100% accuracy, demonstrating that the proposed methodology is practical and promising to be employed in this kind of challenges.
A Comparison Between Three Vision-Based Displacement Measurement Methods in Lab-Scale Structural Experiments
Capturing structural displacement time-histories under service conditions or controlled load tests provides critical information for structural condition assessments. Measuring displacements directly is often impractical due to inherent technical and experimental difficulties, particularly for field inspection of in-service structures. Therefore, quantifying displacements indirectly from localized acceleration or strain measurements has been the dominant measurement approach for decades, despite numerical and logistical implementation challenges. More recently, the development of high-resolution imaging sensors, along with the emergence of high-performance digital image processing techniques, has led to the emergence of computer vision-based displacement tracking methods. These methods, generally referred to as optical flow field measurement, have been demonstrated to work effectively under a variety of laboratory and full-scale field conditions. The measurements are generally less accurate than those obtained from installed sensor arrays, but they have the advantage of providing dense displacement field measurements and do not require direct sensor installation. These approaches can be categorized into three major categories: dense flow, target or invariant feature tracking, and phase-based flow. This paper provides a comparative analysis of these three measurement paradigms for structural monitoring applications. A series of illustrative laboratory experiments are used to highlight key benefits and differences between different approaches.
Damage Without Indication Detection of Tendon Rupture Using Coda Wave Interferometry
Pre-stressing is often the decisive feature of concrete bridges. Deterioration of the tendons used to provide pre-stress is thus an immense problem. To detect potential damage, monitoring of the structures promises to be a remedy. However, since the location of damage is seldom known, conventional monitoring systems reach their limits. Then, ultrasound in conjunction with coda wave interferometry becomes promising to monitor entire structures with just a small number of sensors. To demonstrate the general feasibility of detecting tendon rupture, a pre-stressed concrete beam was experimentally investigated. Failure was artificially initiated by cutting a tendon. Ultrasonic and strain measurements recorded stress changes that occurred due to cross-section losses of the tendon. Both the process of pre-stressing and the artificially induced failure has been tracked using the coda wave technique. Based on the relative velocity change of the ultrasound, the internal state change in the (from the outside) still intact specimen could be detected and tracked. These initial results inspire further in-depth investigations into the detection of pre-stressing steel fractures.
Characterization and Analysis of Constructional Errors of Long-Span Prestressed Steel Structures Based on In-Situ Measurements
Long-span prestressed steel structures, known for its light weight and well performance, are world-wide used and developed nowadays. The constructional errors are challenges to long-span prestressed structures with considerations of the constructional precisions, prestressing degrees, and global stability respectively. The 3D laser scanning technique is applied in the structural health monitoring which is used in the long-span prestressed structures as well. However, a gap exists between measurement point clouds and structural assessments of long-span prestressed steel structures due to the complexity and volume of scanning data. The research targets at the real-time global stability assessments of long-span prestressed steel structures, including characterization of constructional errors from in-situ measurements, establishment of probabilistic model for constructional errors sensitivity study, and real-time constructional errors analysis. This work emphasizes current research progress on constructional errors characterization and data analysis from the in-situ measurements. The in-situ measurement data obtained from two projects of long-span prestressed steel structures charged by the researchers. The constructional errors are smartly recognized from the geometric deviations in comparison with nominal BIM models. The recognized data are then characterized from the convolutional neural network algorithm and statistically analyzed as well. The statistical data is used for the constructional-error sensitivity study where failure probabilities and collapse modes will be carefully evaluated. The research bridges the structural health information and structural stability assessments of long-span prestressed steel structures. In turn, it lays a solid foundation of real-time instant global stability assessments of long-span prestressed steel structures in a long term.
Study on Dynamic Performance and Safety of Vehicle-Track-Bridge System of Continuous Steel Truss Bridges for Long Span High Speed Railway
With the continuous improvement of high-speed train speed and railway bridge span, the safety of long-span high-speed railway vehicle-bridge-track coupling system has attracted extensive attention of bridge workers. In order to study the safety of high-speed railway long-span bridges and running trains, this study established a vehicle-bridgetrack multibody dynamics model of a five-span continuous steel truss high-speed railway bridge, the finite element model was revised based on the measured data of the bridge, and the vibration response of the key positions of the revised bridge model is in good agreement with the measured data, which proves that the established model can well represent the mechanical performance of the actual bridge. Based on the theoretical model, the influence mechanism of train speed and track irregularity on the vertical displacement and acceleration of mid-span section of the bridge, dynamic amplification factor are studied. Then the train safety and stability indexes and the bridge vibration response evaluation standards were used to evaluate the safety of bridge and train under various train speed and track irregularity operating conditions. This has certain guiding significance for the safety and rational design of the long-span high-speed railway vehicle-bridge-track coupling system.
Shear Strength Analysis of Reinforced Concrete Beams with the Influence of Initial Shear Cracks
Cracks in reinforced concrete (RC) structures may result in a reduced loadbearing capacity, and thus understanding the effects of cracks is an important part of a comprehensive structural health monitoring strategy. It is known that the impact of cracks cannot be quantified solely based on the crack patterns. In particular, shear cracks form as a result of a more complex stress state and their influence on the structural performance of RC beams is yet to be studied in detail. This research focuses on the shear cracks and aims to correlate their location to the load-bearing capacity of beams. In an experimental program, initial shear cracks were first generated by loading at varying positions and then the beams were loaded to failure. Four types of beams were tested; these were beams with: (i) no cracks, (ii) inclined cracks in the bending span, (iii) shear cracks in the shear span, and (iv) a pre-defined notch in the shear span. Furthermore, FE analyses were carried out to model the structural behavior of the beams using two different methodologies. The results indicate that compared with reference beams, the initial cracks in the bending span decreased the structural stiffness of RC beams but have limited impacts on the ultimate capacity. However, the cracks in the shear span not only decreased the stiffness, but also significantly reduced the load-carrying capacity of the beams. The observed behaviors were also reasonably well captured in FE analyses.
Multifunctional Composite Rebars for Distributed Structural Health Monitoring of Concrete Structures
Structural health monitoring (SHM) of reinforced concrete structures is a rapidly developing field with significant advancements over the last decade. Most of the existing SHM systems rely on a large number of point sensors attached to or embedded inside the structures. The sensor network should be densely distributed on the structure or around the potential damage areas for an acceptable damage detection performance. Apparently, this is not an optimal solution for an effective cost. On the other hand, issues such as securing a reliable power supply, cable connections and maintenance costs have remained a major challenge for current distributed SHM systems. To cope with these challenges, we propose a novel concept of multifunctional composite reinforcement rebars with sensing and energy harvesting functionalities for reinforced concrete structures. We develop multifunctional rebars via introducing a triboelectric nanogenerator (TENG) technology into the fabrication process of fiber reinforced polymer (FRP) rebars. The developed rebars with built-in TENG mechanism serve both as nanogenerators and distributed sensing systems under external mechanical vibrations. We perform experiential studies to verify the electrical and mechanical performance of the developed self-powering and selfsensing composite rebars.
System Identification for Modal and Flutter Analysis of an Inflatable Wing
The main objective of the presented work is to understand the aeroelastic characteristics of an inflatable kite. Experimental modal analyses of two test articles are conducted using system identification and signal processing. To understand the flutter behavior of inflatable wings, flutter test articles made from Nylon and Dyneema fabrics are tested first for their modal properties and then for flutter in a wind tunnel. Various experimental techniques are implemented for understanding the modal responses of these two test articles. Instruments such as a 3D-photogrammetry camera system and accelerometers are used to measure the dynamic and static responses of these test articles. Using MATLAB's System Identification Toolbox and Signal Processing Toolbox, the modal parameters are identified from measured responses, such as out-ofplane displacement and accelerations. The experimental and operational modal parameters are then used to estimate the modal responses. The Zimmerman- Weissenburger flutter margin parameter is used to predict the onset of the flutter modes from the identified modal parameters. The verified system identification technologies are leveraged to understand the aeroelastic dynamic instability of a tethered inflatable wing during a wind tunnel test.
Flexible Stretchable Mechanoluminescent- Perovskite Sensor for Structural Health Monitoring
Previously, we reported a flexible thin-film sensor realized through the combination of mechanoluminescent (ML) material with perovskite, which consists of multiple layers to harvest ML light. In this work, a thin film stretchable sensor is constructed through a simple manufacturing technique. The sensor comprises a thin functional layer of copper-doped zinc sulfide (ZnS:Cu) as a light emitter and a thin perovskite layer as a light absorber. The perovskite layer is constructed on a thin layer of poly(3,4- ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), which is developed on a stretchable polydimethylsiloxane (PDMS) substrate. Atop the thin perovskite lies a thin eutectic gallium indium (EGaIn) layer acting as an electrode. The simple selfpowered stretchable sensor allows easy fabrication and is a non-complex sensor architecture. Here, ZnS:Cu emits lights and perovskite immediately absorbs the light and converts it to electrical current. The output current of the sensor increases with an increase of applied strain. The self-powered sensor may find many applications in foot ulcer detection, prosthetic/artificial electronic skins, touchpad, and structural health monitoring of large composite structures.
Variable Stiffness Honeycomb Metamaterials for Adaptive Ankle Brace Design
Lateral ankle sprains cost billions of dollars in medical expenses annually and frequently result in long-term functional decline and a diminished health-related quality of life. While ankle braces have been shown to be effective in prophylaxis of subsequent ankle sprains, current braces are either too stiff and affect normal gait or too flexible and provide insufficient support during high-intensity activities. In this study, we proposed an adaptive ankle brace design that employs dynamically variable stiffness components to provide minimum support under normal gait movements and maximum rigidity under large ranges of motion. To achieve these unique properties, a honeycomb geometry was designed and three dimensionally printed with thermoplastic polyurethane to exhibit nonlinear, strain-stiffening, elastic behavior. We conducted a series of tensile load tests on different honeycomb unit cell configurations. First, the influence of unit cell designs on their mechanical strength and force-strain profiles was characterized. Second, experimentally calibrated finite element models of individual components simulated the mechanical response of the geometry, which were then used to optimize the geometrical parameters of the honeycomb shape (i.e., ring size, length of lateral elements, and thickness). The results identified promising design parameters for these honeycomb geometries that could be used to realize next-generation adaptive ankle braces.
Self-Sensing and Self-Powering Multifunctional Structures
Developing lightweight multifunctional structures with sensing, energy harvesting and mechanical tunability capabilities has been the holy grail for scientists. This study presents our vision toward the next stage of the technological revolution in multifunctional structures science where a so-called Engineered Self-aware Structure (ES2) can sense, empower and program itself using its constituent components. We discuss the creation of such smart structures through the fusion of advanced metamaterial and energy harvesting technologies. The ES2 concept is validated via designing a composite beam prototype. The results imply that the fabricated multifunctional beam can measure the amplitude of the applied load and harvest the energy from the mechanical excitations. We demonstrate the broad application of the proposed concept in aerospace, biomedical, and civil engineering areas for designing multiscale self-sensing and self-powering devices.
A Comparison of Sensing Technologies Detecting Damage and Changes in Smart Concrete
This paper presents results of a study of smart concrete with a combination of surface-mounted lead zirconate titanate (PZT) piezoelectric transducers and electrical resistivity-based sensing systems. Tests were performed to identify their sensitivities to concrete properties including cure state, load, and cracking. The smart concrete slab was created with added graphene nano-platelets, internal electrodes, and fiber reinforcement supporting electrical resistivity mapping. Electrical resistivities were measured over the curing time and with external loads applied to the cured specimens. The test results indicated that electrical resistivity was effective in detecting curing conditions of cement-based material. In addition, both piezoelectric transducers and smart concrete were effective in detecting cracks generated during a three-point bending test.
Optimization of a Structural Health Monitoring Systems Integration in Laminated Composite Cured in Autoclave
Composites represent approximately 50% of the weight of structural parts in new aircraft as Airbus 350 or Boeing 787. Damages could occur on these parts and their monitoring is required for the safety of users. A Structural Health Monitoring system composed by a Lambs waves generator and a sensor is a privileged candidate to detect such damages. The flexibility of composite manufacturing allows the integration of such a system. The topic of this study is to optimize the integration of a Structural Health Monitoring system in aircraft structural parts. The present article focuses on the optimization of the integration method of a piezoelectric Lead Zirconate Titanate transducer into laminated composite (Carbon/Epoxy), cured in autoclave according to aircraft manufacturing requirements. The health state of the integrated transducer with three connecting methods is evaluated using X-ray scanning. Punctual stress is responsible for the crack of the transducer occurred during composite curing. The connecting method with aluminum sheet and silver joint is selected because it minimizes local stresses and keeps the transducer integrity. The cohesion between the integrated transducer and the host material is observed by optical microscopy, it shows the presence of void zones located in the PZT edges. A Laser Doppler Vibrometer scanning shows the ability of the integrated piezoelectric transducer to generate Lamb waves.
A Multi-Headed CNN Framework for Lamb Wave-Based Damage Detection in a Thin Aluminum Plate
In this research, a deep learning framework of 1D-Convolutional neural networks (CNNs) is utilized to recognize and auto-extract damage related features within the fragments of raw 1-D Lamb wave data. To analyze the performance of the proposed network, a diverse database is constructed to train and test the 1D-CNN architecture. It contains Lamb waves time-series data recorded from both 24 experimental and 216 Finite Element (FE) simulation setups of 1.6 mm thin Al-5052 plate. To scan the platestructure, a pitch-catch transducers configuration is adopted. The actuator-transducer excites 3.5 cycles modulated sinusoidal tone-burst signal at three different frequencies i.e., 100 kHz, 125 kHz and 150 kHz and the corresponding responses are collected from sensor-transducer only. An anomaly is introduced in the FE model in the form of a 0.8 mm deep and 2 mm wide notch-like cavity. Whereas, the damage in the experimental setup is realized by attaching an external mass of 14 gm in the center of the PZTs. Next, a 1D-CNN architecture is designed that uses raw 1D Lamb wave signals as an input and can capture high-level damage related features from the raw Lamb wave signals. Then the CNN model is trained on 90% of the total number of samples in the database using the Adam algorithm. At a later stage, the trained 1D-CNN architecture performance is validated against the 24 unseen Lamb wave responses. Out of the 24 unseen samples, the proposed architecture decisively predicts the outcome for 23 samples. This test against the unseen experimentally generated samples proves that the proposed deep learning framework has achieved the required generalization over the real scenario.
Lightweight Deep Learning Model of Semantic Segmentation for Complex Concrete Cracks in Actual Bridge Inspection
Recently, extensive studies have been performed for crack detection and segmentation using deep learning and computer vision techniques to accomplish autonomous bridge inspection. These deep network models are frequently trained with a large volume of parameters to ensure good performance. However, the robust applications under real-world situations of actual bridge inspection still face significant challenges. For example, false-positive recognitions of complex background disturbances excluded in the training sets are inevitable to exist. Besides, the real-time requirement for deploying large-volume deep networks in edge computing equipment is still challenging to achieve. This study establishes a lightweight semantic segmentation model for complex concrete crack segmentation in actual bridge inspection. First, the DeepLabv3+ model is adopted as the baseline, and the backbone module is replaced by MobileNetV2 instead of ResNet101. Second, the depthwise separable convolution, atrous convolution pyramid, and inverted residual modules are utilized to reduce convolutional parameters, expand receptive fields, and alleviate gradient vanishing, respectively. Third, the dataset is enhanced with negative disturbance examples, including straight-line-like structural edges and exposed rebars, to improve the model performance against false positives without additional labeling workload. Original images with different resolutions are first collected from actual bridges, and negative samples are further added to the dataset. A total of 4303 patches in 512 × 512 are generated by a sliding window, where 3443, 430, and 430 are randomly selected for training, validation, and test. Ablation experiments demonstrate the necessity and effectiveness of using MobileNetV2 instead of ResNet101 as the backbone and adding negative examples into the dataset. The results show that the mean intersection-over-union (mIoU) for crack segmentation in various real-world scenarios reaches 0.759. The recognition rate of false positives for complex background disturbances is effectually suppressed by introducing straight-line-like structural edges and exposed rebars into the dataset. Furthermore, the average time cost gains a significant reduction of 35.1% using the established lightweight crack segmentation model with only a slight drop on IoU of 0.017.
A Novel Fatigue Damage Sensor for Stress/Strain-Life Based Prediction of Remaining Fatigue Lifetime of Large and Complex Structures: Aircrafts
In this paper, a new novel smart fatigue damage sensor (US Patent 8,746,077 B2) for continuous monitoring of fatigue health state of structural members of aircrafts is presented. The sensor has multiple parallel beams, each sensitive to different levels of fatigue lifetime. These beams are designed to fail prematurely but progressively as the sensor goes through the same fatigue cycles as the structural member it is attached to. Whenever fatigue level on an individual beam of the sensor exceeds the number of engineered fatigue cycles, that particular beam fails and sensor electronics can detect that failure and transmit this information wirelessly. Just like mileage signs on the road informing you about the distance left to your destination as you drive, multiple beams of the sensor serve similar purpose informing the user about the distance to failure progressively. Just as mileage signs can be placed at desired intervals, multiple beams can be engineered to give indication at desired fatigue milestones. This gives ability to monitor aging status of the structure and also help schedule predictive maintenance accordingly. The beams inside the sensor are designed to work based on different stress concentration factors (Notch Factors)/geometry to measure the level of structural fatigue health. The sensor needs to be mounted on the surface of structural member at fatigue critical locations just like strain gauges. Unlike strain gauges, a unique feature of the new sensor is its ability to operate without power source. This way it can serve for a long time without maintenance. Since sensor does not need power to operate, it can be embedded or mounted on critical components including composite structures or rotating helicopter shafts, gears, etc. After being attached to critical location of the real structure, the smart fatigue damage sensor goes through the same fatigue life experience of critical structural elements or mechanical components from the beginning of service life to the end. The fatigue sensing beams with different stress-strain and fatigue lifetime levels are designed to estimate the fatigue damage accumulation and remaining fatigue life of unidirectional and multidirectional structural or mechanical elements including composite structures. Since distributed fatigue sensor network system monitors the fatigue health conditions of structures periodically or on demand, the collected data can be used not only for condition-based fatigue life prediction but also for sensor based predictive fatigue maintenance and development. This new approach could also pave way to new fatigue design tools for fatigue sensitive complex, large and expensive engineering structures or mechanical systems of aircraft structures. Full paper will be concentrating design principles of the sensor based on Stress/Strain-Life Based Prediction principles.
A Cyber-Physical System Based Real-Time Fault Diagnosis of Induction Motors
Induction motors are one of the major electrical prime movers in industrial sectors. Since these motors are operated continuously, they are subjected to wear and tear which lead to faults at a later stage in its life. These faults which arise can be classified into 5 major categories i.e., broken rotor bars, stator winding faults, air-gap eccentricity, bearing faults, and torque fluctuations. A failure in induction motors leads to machine downtime, increased maintenance costs, and puts the lives of the plant personnel at risk, thus leading to undesirable consequences. Hence, uninterrupted operation of the machine is the need of the hour for which real-time condition-based monitoring of induction motors needs to be implemented. Industries are making an attempt to tap into the technology that involves around cyber-physical systems (CPS) and access real-time information regarding the motor health condition. The present article explores the CPS structure for real-time fault identification so that appropriate action can be taken by plant personnel. The CPS technology is a modular framework, which consists of a current sensor that transmits data to a remote minicomputer (e.g., Intel NUC kit) or a microcontroller (e.g., Raspberry Pi) by processing it through a data acquisition (DAQ) system across a wireless network. Since the range of defect frequencies for fault diagnosis in these induction motors is 5 kHz, Nyquist sampling frequency (ð¹ð‘ ) for data acquisition should at least be 10 kHz. It is to be noted that a microcontroller can be of low cost; however, maintaining ð¹ð‘ more than 500 Hz tends to cause random jitters at the core of the operating system (OS). As a result, the signal-to-noise ratio (SNR) is compromised in microcontrollers leading to incorrect post-processing of the current time-stamp data for motor fault diagnosis. Hence, in the present article, a minicomputer is used for data acquisition of current time data at ð¹ð‘ of 10 kHz and infer the motor health status by investigating the current spectrum. The information of motor health condition is stored in comma-separated values (CSV) file, which is further transferred over Google Cloud Storage (GCS) via hypertext transfer protocol (HTTP) with transport-layer security (TLS) encryption. HTTP converts the CSV data file into binary format and maintains the record of meta-data of the files. Meta-data essentially keeps track of when the file was created in the remote minicomputer. Additionally, in order to ensure a high data transfer rate at a given instant of time, the HTTP file transfer protocol divides the actual data into small chunks that are subjected to parallel composite uploads. When the data is collected in the computer at the receivers end i.e., the plant personnel in the present case, the data is recreated back to the original CSV file. As a result, the concerned plant personnel has complete information about the specific motor which has started failing and prevents any major breakdown of the machine. Thus, the fault detection of the motors at the incipient stage through CPS technology helps in developing an effective process that aids in the smooth functioning of the machines.
Bridge Slab Anomaly Detector Using U-Net Generator with Patch Discriminator for Robust Prognosis
More than 50 years aging civil infrastructures have deteriorated, then structural diagnosis and periodic prognosis become critical for predictive maintenance. In terms of the bridge inspection every 5 years in Japan, we have collected a lot of human eye inspection. In context of digital structural monitoring, in addition to the past human inspection we make the most of drone flight images. However, human subjective judge includes individual bias, then a measurable objective score should be quantified using a unified anomaly distance from a health condition. Supervised learning, e.g. classification and semantic segmentation method is not always robust for unseen data. If we address the unlearned blind feature without any experience, prediction error might be a higher hurdle to overcome low precision and less recall problem. The generative anomaly detection via unsupervised learning has been growing in various fields, e.g. medical, manufacturing, food, and materials. If the distance and angle to the target damage interest could be controlled among a feasible range, and if the background noise could be removed and relaxed, then concrete surface damage and steel paint peel or corrosion would enable to discriminate them for predictive maintenance. In this paper, we propose a steel anomaly detector method to compute anomalous scores automatically, where we customize several U-shape skip-connected generator network with patch GAN discriminator. Exactly, we have create an encoder-decoder network using the VGG19 based U-Net generator with a patch discriminator. Furthermore, we explore robust unified anomaly score indicator for the target concrete and painted steel parts to analyze deterioration prognosis, so as to monitor the current status far from a health condition. Finally, focusing on the bridge slab, we exploit toward the inspection images with the number of 10,400, where they contains reinforcement concrete slab at 400 bridges under the direct control of national managers. In order to be stable learning and robust structural health monitoring, we demonstrate to visualize several anomalous feature map for precisely and full-covered digital inspection.
A Hierarchical Domain-Adversarial and Multi-Task Learning Algorithm for Bridge Damage Diagnosis Using a Drive-By Vehicle
Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as no need for directly installing and maintaining sensors on the bridge. However, many of the existing drive-by monitoring approaches are based on supervised learning models that require labeled data from every bridge of interest, which is expensive and time-consuming, if not impossible, to obtain. This labeling requirement is further exacerbated by having multiple diagnostic tasks (e.g., damage detection, localization, and quantification) because they have different learning difficulties. To this end, we introduce a multi-task domain adaptation framework that transfers the damage diagnosis model learned from one bridge to a new bridge without requiring any labels from the new bridge in any of the tasks. Our framework trains a hierarchical neural network model in an adversarial way to extract task-shared and task-specific features that are informative to multiple diagnostic tasks and invariant across multiple bridges. We evaluate our framework on experimental laboratory data collected from 2 bridges and 3 vehicles. We achieve accuracies of 95% for damage detection, 93% for localization, and up to 72% for quantification, which are ~2 times improvements from a baseline method.
Natural Synchronization of Wireless Sensor Networks for Structural Health Monitoring
Time synchronization in communication networks is a common issue: in a sensor network it means that the order of data samples becomes uncertain, which can make it unusable. Dedicated signals and schemes for synchronization of sensor networks has hence been a well-researched topic for decades. Here we bring in an approach to synchronization which uses the sensory data. Drawing inspiration from sensor time synchronization using environmental noise, we consider synchronizing sensory nodes for structural health monitoring if the physical quantity the sensors measure is correlated, propagating as a wave, or oscillating in regular fashion, it is intuitively clear how to put it to use. We discuss when structural health monitoring signals can aid synchronization; we also connect this synchronization scheme to the idea of using physical human-made structures as reservoirs for reservoir computing, formulating synchronization as a reservoir computing task.
Assessment Criteria for Optimal Sensor Placement for a Structural Health Monitoring System
Machine learning algorithms have been extensively used to implement structural health monitoring (SHM) systems to detect the occurrence of damage within a structure. To obtain the most effective data for SHM decision making, it is desirable to perform sensor placement optimisation (SPO), with a particular focus on damage identification. However, comparatively little attention has been paid to systematic assessment criteria appropriate to the design of a sensor system for SHM. This paper focusses on studying the evaluation criteria at different stages of a sensor-system design process, ranging from the measurement of linear associations to the detailed evaluation of the overall probability of correct classification. The effects of the investigated criteria are demonstrated using a physics-based model with uncertain parameters related to material proprieties. Predictions of the dynamic response of the structure in different states of interest are used to derive features.
On Normalisation for Domain Adaptation in Population-Based Structural Health Monitoring
The practical application of structural health monitoring (SHM) is often limited by the unavailability of labelled data. Transfer learning - specifically in the form of domain adaptation gives rise to the possibility of leveraging information from a population of physical or numerical structures, by inferring a mapping that aligns the feature spaces. There are a number of approaches to domain adaptation that minimise some distribution discrepancy metric. However, it is found that under high initial distribution discrepancy, these methods may be prone to performance degradation. In this paper, guided normalisation is proposed as a solution to the initial distribution discrepancy problem. Several case studies demonstrate how normalisation can itself perform powerful adaptation and facilitate further adaptation, with more sophisticated domain adaptation methods.
On the Application of Variational Auto Encoders (VAE) for Damage Detection in Rolling Element Bearings
In structural health monitoring (SHM) and condition monitoring (CM) applications, the expense of testing programmes may be too high to obtain adequate datasets. When limited by the number of available data samples, one may rely on dimensional reduction methods to proceed with a meaningful statistical and probabilistic analysis. In this work, some state-of-the-art dimensionality-reduction techniques were investigated as part of a simple ball-bearing damage detection problem. A variational auto-encoder (VAE) was compared to other methods, based on their capability to generate low-dimensional representations of the data. Unlike other common alternatives, such as principal component analysis (PCA) or auto-encoding (AE) networks, the VAE introduces a probabilistic framework via the latent embeddings. A well-defined distribution is thereby constructed on the latent variables, making the transformed dataset an optimal one for subsequent pattern recognition analysis. The results demonstrated an increase in classification performance given the low-dimensional representation generated by the VAE.
Structural Model Updating Using Variational Inference
Monte Carlo sampling approaches are frequently used for probabilistic model updating of physics-based models under parametric uncertainty due to their high accuracy. The model updating framework produces a model that represents the real system more accurately than the prior knowledge or assumptions. This statistically updated model may prove useful if Structural Health Monitoring (SHM) techniques are to be applied. However, the updating of the models requires the use of a high number of samples, implying a high computational cost. Another additional disadvantage of these methods is that most of them require the calibration of a high number of parameters for their algorithm to become sampling efficient. Variational inference (VI) is an alternative approach for inference often used by the machine learning community. An optimization algorithm is employed to choose from a family of distributions the member that best approximates the posterior. In the method described in this paper the variational posterior that maximises the evidence lower bound (ELBO) is chosen. An approach based on VI is proposed and implemented on two different numerical examples to infer the uncertain parameters by postulating a variational posterior distribution given by a multivariate Gaussian approximation. It has been found that the number of samples required for the calculation of the posterior is reduced compared with Monte Carlo sampling approaches, however this occurs at the cost of some accuracy. The methodology will be helpful for the development of enhanced SHM strategies that require fast inference under a limited computational budget.
Population Based Pumps Monitoring and Benchmarking Using IoT and Edge ML Learning Methods
Machinery monitoring is typically applied to a single machine based on sensor integration and data analysis. Such an approach to a set of machines operating in similar conditions allows for a multivariate analysis for condition monitoring based on a single machine as well as based on group analysis. This paper describes an Industrial Internet-of-Thing (IIoT) concept for condition monitoring of machinery population based on water pumps. The first part provides an introduction to unsupervised anomaly detection based on population modeling with using features calculated from the: mechanical (based on vibration sensors), electrical (voltage and current signals collected from electric motors that drive monitored pumps) and operational processes (such as pressures, flows) signals. Finally, the preliminary results from laboratory testing and demonstration at a wastewater processing plant are presented.
Applications of Surrogate Finite Element Machine Learning Approach for Structural Monitoring
Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. This problem is particularly relevant in the development of autonomous vehicles, especially in the concept of urban air mobility. The actual usage of the vehicle will be used to predict stresses in the structure and therefore to define maintenance scheduling. Supervised regression machine learning algorithms are used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system, therefore creating a surrogate of the finite element model. The paper will present applications of the approach to a one-dimensional beam structure, modeled with finite element methods. Based on the response of the beam measured at a few reference locations, the surrogate finite element approach determines the entire response of the beam at all spatial locations (displacements, velocities, accelerations, stresses, strains) using neural networks. The FEA-based machine learning approach estimates the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe and efficient maintenance procedures. The effect of type of input features and output and their relationship on the performance of the neural network is discussed, as well as the effect of the beam boundary conditions on network performance.
Scalable Impact Detection and Localization Using Deep Learning and Information Fusion
Due to their unpredictable nature, many impact events (e.g., overheight vehicles striking on low-clearance bridges) go unnoticed or get reported hours or days later. However, they can induce structural damage or even failure. Therefore, prompt impact detection and localization strategies are essential for early warning of impact events and rapid inspection of structures. Most existing strategies are developed for aircraft composites panels utilizing high rate synchronized measurement from densely deployed sensors. Limited efforts are made for other applications, such as infrastructure systems or extraterrestrial human habitats, which require large-scale measurement and scalable detection strategies. Particularly in harsh environments, structural impact localization must be robust to limited number of sensors and multi-source errors. In this study, an effective impact localization strategy is proposed to identify impact locations using limited number of vibration measurements. Convolutional neural networks are trained for each sensor node and are fused using Bayesian theory to improve the accuracy of impact localization. Special considerations are paid to address both measurement and modeling errors. The proposed strategy is illustrated using a 1D structure, and numerically validated for a 2D dome-shaped structure. The results demonstrate that the proposed method detects and localizes impact events accurately and robustly.
A State-Parameter Estimation in Two Engineering Domains: An Extended Kalman Filter Approach
This paper intends to present a synthesis of works based on the study of the behavior of the Kalman filters in two different domains. The first area is dedicated to the flocculation process occurring in water treatment. The second one covers aircraft structural damage based on SHM approach. The general methodology consists in modeling a state-parameter observer to perform estimations using the joint EKF. The robustness and efficiency of the Kalman filters is addressed in order to allow a cross-fertilization in the two area. Model propagation and accurate prognostics are therefore allowed due to the improvement of model parameter knowledge. In the context of the flocculation, the prediction of the time evolution of the characteristic diameters is much more efficient than QMOM. For fatigue damage prognostic, the best initial conditions leading to accurate estimation are highlighted according to materials. Whatever the problem is, the estimation error magnitude is known.
On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation
A major problem of structural health monitoring (SHM) has been the prognosis of damage and the prediction of the remaining useful life of a structure. Both tasks depend on multiple parameters, many of which are often uncertain. A wide range of models have been developed for the aforementioned tasks, but they have been either deterministic or stochastic with the ability to take into account only a restricted set of past states of the structure. In the current work, a generative model is proposed in order to make predictions about the damage evolution of structures. The model is able to perform in a population-based SHM (PBSHM) framework, to take into account many past states of the damaged structure, to incorporate uncertainties in the modelling process and to generate potential damage evolution outcomes according to data acquired from a structure. The algorithm is tested on a simulated damage evolution example and the results reveal that it is able to provide quite confident predictions about the remaining useful life of structures within a population.
Corrosion Prognostics for Offshore Wind- Turbine Structures Using Bayesian Filtering with Bi-Modal and Linear Degradation Models
New offshore wind farms are often operating far from the shore and under challenging operating conditions, making manual on-site inspections expensive. Therefore, there is a growing need for remote condition monitoring and prognostics systems for such offshore wind farms. In this paper, we focus on corrosion prognosis since corrosion is a major failure mode of offshore wind turbine structures. In particular, we propose two algorithms for corrosion prognosis by employing Bayesian filtering techniques, one is based on linear degradation and another is based on a bi-modal corrosion model. Due to distinct characteristics of the two degradation models, different Bayesian filtering implementations are therefore required. Although the degradation model of the latter method more accurately reflects the ground truth, we find that the former prognosis method is computationally more efficient and likely more robust against various noise sources.
Comparison of Error Measures and Machine Learning Methods for Strain-Based Structural Health Monitoring
The development of aircraft structures requires many fatigue tests. These tests are usually carried out to validate the corresponding finite element and damage models and to prove the expected damage-tolerant behavior. Monitoring aircraft structures requires experienced staff and is very time-consuming and expensive as the recurring inspection of the structure is a tedious task. We propose a machine learning-based approach that exploits continuous load and strain measurement data to support structural health monitoring and to shift the inspection program towards predictive maintenance. The machine learning model is used for mapping loads onto local strains. With the trained model, different error measures between current measurements and the predicted values are determined. When a specific threshold value based on an error confidence level is exceeded, an alarm is set off, and appropriate actions can be taken. The approach is applied to several fatigue tests with two different types of structures and damage mechanisms. Various error measures and models are compared. The paper shows that, first, simple error measures, such as the root mean squared error, are sufficient and even outperform more sophisticated error distances for detecting cracks with continuous strain measurements. Second, the standard deviation of strain or rather the load-strain slope is a key feature to detect cracks. And third, machine learning models enable structural health monitoring with sensors that even have only small strain values.
Research on Model Updating Method of High- Speed Maglev Guideway Based on Wavelet Transform and Optimization Algorithm
Because the maglev guideway is a vital infrastructure of high-speed maglev train, it is of great significance for the guideway structure to analyze dynamic response and assess structural health of the guideway in research. This paper proposed a model updating method of the high-speed maglev guideway based on wavelet transform(WT) and optimization algorithm, which are used to analyze the dynamic response and assess health. Taking the 600 km/h high-speed maglev test vehicle as the excitation, the field test was carried out on the High-speed Maglev Test Line. The measured dynamic responses of the guideway were obtained, and the measured modal parameters were identified by WT. The finite element(FE) model of guideway was established, considering the elastic boundary conditions and material properties, and the initial modal parameters of the guideway were obtained. Based on the FE model, the response surface model of the guideway was constructed and the objective function of the simulated modal parameters and the measured modal parameters was established. Considering the constraint conditions, the optimal solution of the objective function was found by the optimization algorithm, and the elastic boundary conditions and material properties of the FE model were optimized and updated. The research results indicated that the dynamic response of the updated FE model was highly correlated with the measured dynamic response and proved strongly the effectiveness of the proposed method. A more accurate model updating method for the dynamic response analysis and health assessment of the high-speed maglev guideway was provided by this paper.
Application of a High-Speed Non-Contact Ultrasonic Technique Coupled with Statistical Data Redundancy for Rail Inspection
This paper presents a high-speed non-contact rail inspection technique that has been tested on the field at speeds up to 80 mph. The technique utilizes an array of capacitive air-coupled ultrasonic transducers in continuous recording mode to extract a reconstructed transfer function for a rail segment in a passive manner. The passive approach utilizes the ambient excitation of the rail induced by the wheels of the test car and eliminates the need of a controlled source. A normalized cross correlation operator with modified Welch's periodogram technique is used to extract the transfer function which is independent of the frequency spectrum of the random excitation source (wheels). Presence of discontinuities in the rail reduces the signal-to-noise ratio of the reconstructed transfer function which is statistically tracked using an outlier analysis for multiple reconstructions along the inspected rail. Data from multiple transducer pairs are compounded in the statistical outlier analysis which ensures removal of bias from the data. An adaptive baseline model from pristine rail is used to compute a parameter called the Damage Index (DI) to determine if the probed rail segment has a discontinuity. Raw ultrasonic signals comprising of thousands of data points for a given recording time within a rail segment are therefore compressed statistically into a single DI parameter. Full-scale field tests were carried out at testing speeds of up to 80 mph. Discontinuity detection performance in terms of identifying joints, welds and known transverse defects through Receiver Operating Characteristic (ROC) curves were studied for a range of varying operational parameters such as raw signal strength, baseline length, and testing speeds. Data from multiple passes of the train over the same rail segment were compounded to further introduce redundancies and increase the rate of true detections and reduce the rate of false alarms.
Physics-Enhanced Damage Classification of Sparse Datasets Using Transfer Learning
High-rate, high-acceleration dynamic events produce especially limited and sparse data for two main reasons: high-acceleration loadings can destroy the test article, and the required laboratory equipment is typically expensive and complicated to operate. In many cases, these limitations prevent researchers from collecting additional data, driving the need for machine learning algorithms that utilize small datasets. Despite deep learning's preference for thousands or millions of training examples, the dataset considered in this work contains only six independent examples. Finite element analysis software simulates the dynamic response of an electronic structure, supplementing this small dataset with additional training examples. A hybrid deep learning model first learns the dynamic response of the simulated structure and is then adapted to predict the actual electronic structure's damage levels. This work shows that physics-enhanced transfer learning improves structural damage classification accuracy (ð‘ƒ = 0.0879).
Structural Active Control Framework Using Reinforcement Learning
To maintain structural integrity and functionality structures are designed to accommodate operational loads as well as natural hazards during their lifetime. Active control systems are an efficient solution for structural response control when a structure is subjected to unexpected extreme loads. However, development of these systems through traditional means is limited by their model dependent nature. Recent advancements in adaptive learning methods, in particular, reinforcement learning (RL), for real-time decision-making problems, along with rapid growth in high-performance computational resources, enable structural engineers to transform the classic modelbased active control problem to a purely data-driven one. In this paper, we present a novel RL-based approach for designing active controllers by introducing RL-Controller, a flexible and scalable simulation environment. RL-Controller includes attributes and functionalities that are necessary to model active structural control mechanisms in detail. We show that the proposed framework is easily trainable for a five-story benchmark linear building with 65% reductions on average in inter story drifts (ISD) when subjected to strong ground motions. In a comparative study with an LQG active controller, we demonstrate that the proposed model-free algorithm learns actuator forcing strategies that yield higher performance, e.g., 25% more ISD reductions on average with respect to LQG, without using prior information about the mechanical properties of the system.
Hybrid Supervised Machine Learning Approach for Damage Identification in Bridges
Structural health monitoring (SHM) of bridges often involves machine learning algorithms, trained based on two independent learning strategies, namely unsupervised and supervised learning, depending on the type of training data available. When unsupervised learning strategy is employed, the algorithms are normally trained with data gathered from monitoring systems, corresponding to normal operational and environmental conditions. The lack of information regarding the dynamic response of the structure under extreme environmental and operational conditions, as well as under damage scenarios, may lead to flaws in the damage detection process, namely the rise of false indications of damage. In order to overcome this drawback, finite element models can be used as structural proxies to generate data that correspond to scenarios unlikely to be recorded by the monitoring systems, such as extreme temperatures or structural damage. The use of both monitoring and numerical data in the framework of a hybrid approach greatly improves the quality of the training process, as recently shown by the authors. The hybrid approach also enables the use of the supervised learning strategy if numerical data corresponding to damage scenarios are available. Therefore, this paper assesses the reliability of a hybrid approach for the supervised training of machine learning algorithms using numerical data corresponding to extreme temperatures and several damage scenarios. The damage scenarios comprise various degrees of settlement of a bridge pier and a landslide near the same pier. Monitoring data are used for the testing of the algorithms and for the initial calibration of the finite element model, which does not need to be exceedingly detailed, as the probabilistic variation of the uncertain parameters is taken into account. The procedure was applied to the Z-24 Bridge, a well-known benchmark consisting of one year of continuous monitoring and including progressive damage readings.
Active Deep Learning-Based Corrosion Damage Detection in Aircraft Structures
Lamb wave-based damage detection has been demonstrated to be an efficacious method for structural health monitoring (SHM) in general, and corrosion in particular, and is thus deployed in this study. Since a large amount of data is needed for the deep learning networks, this study relies heavily on simulations as the data source and the waveforms are thus generated using simulations. The propagation of the Lamb waves is determined by finite element analysis which is carried out using ABAQUS. The signal features are extracted using continuous wavelet transform for amplitude change observation for presence and extent of the damage. One of the key aspects this paper focuses on is the application of the SHM methodology proposed here for realistic dimensions of corrosion pits. Thus, damage sizes are considered which fall in the range of pitting corrosion morphologies. Simulations are carried out with idealized corrosion pits of varying depths. Methods based on Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) are used for the inverse problem solution to find the damage parameters and are compared with the numerical results. The results show much promise and could be a viable means of detecting corrosion in aircraft structures.
Rail Neutral Temperature Estimation Using Zero Group Velocity Modes and Machine Learning
With increasingly frequent extreme heat events over the past half century, thermal stress measurement and management of continuous welded rail (CWR) have become more important for railroad maintenance. Methods, including visual inspections and rail lifting, are routinely performed in railroad networks of the U.S. to prevent rail thermal buckling. When intervention becomes necessary, a rail distressing process, involving rail cutting and welding, will be performed to re-establish the zero-stress state at a desirable temperature. And the temperature at which the rail is stress-free is defined as rail neutral temperature (RNT). In this work, an RNT predictive tool that exploits zero group velocity (ZGV) modes and machine learning is proposed. First, the existence of ZGV modes in CWR is investigated through numerical simulation, using both semianalytical finite element analysis (SAFE) and finite element (FE) models. Further, parametric studies are performed to quantify the effect of axial loads and rail temperature on ZGV modes. Additionally, the team established an instrumented field test site at a revenue-service line and performed multi-day data collection to cover a wide range of temperature and thermal stress levels. FE models were calibrated based on the field-collected vibrational data via a linear program optimization approach and an excellent agreement between model and experimental results was obtained. Finally, a supervised learning framework was developed to estimate the RNT using rail temperature and resonance frequencies as the inputs. The results show that the proposed framework can provide RNT estimation with reasonable accuracy (±5 ºF) when measurement noise is low.
Simulation of Guided Waves for Structural Health Monitoring Using Physics-Informed Neural Networks
Guided wave propagation is a valuable and reliable technique for structural health monitoring (SHM) of aerospace structures. Along with its higher sensitivity towards small damages, it offers advantages in traveling long distances with minimum attenuation. Simulation of guided wave propagation is essential to understand wave behavior, and calculating the dispersion relations forms an integral part of the procedure. Application of the current numerical techniques for complex media is highly involved and faces issues related to accuracy, stability, and computational resources. Development in the field of machine learning and graphical processing units (GPUs) leads to the implementation of a faster, automated, and scalable deep neural networks-based learning approach for such problems. Most of the implementation in the field is based on data collection and uses neural networks for nonlinear mapping from input space to target space. However, a large amount of prior information in the form of a governing differential equation is not utilized. In this paper, we have used Physics-Informed Neural Networks (PINNs), in which neural networks are utilized to solve governing partial differential equations. PINNs are implemented to obtain the solution of a one-dimensional wave equation with Dirichlet boundary conditions. The exact solutions and predicted responses match closely with lower mean square error in limited computational time. We have also conducted a detailed comparison of the effect of neural architecture on the mean square error and the training time. This study shows the merit of deep neural networks leveraging the available physical information to simulate the wave phenomenon for SHM efficiently.
Full-Scale Deformation Field Measurements via Photogrammetric Remote Sensing
3D remote sensing technologies have improved dramatically over the past five years and methods such as laser scanning and photogrammetry are now capable of reliably resolving geometric details on the order of one millimeter or less. This has significant impacts for the structural health monitoring community, as it has expanded the range of mechanics-driven problems that these methods can be employed on. In this work, we explore how 3D geometric measurements extracted from photogrammetric point clouds can be leveraged for structural analysis and measurement of structural deformations without physically contacting the target structure. Here we present a non-destructive evaluation technique for extracting and quantifying structural deformations as applied to a load test on a highway bridge in Delaware. The challenging nature of 3D point cloud data means that statistical methods must be employed to adequately evaluate the deformation field of the bridge. Overall, the results show a direct pathway from 3D imaging to fundamental mechanical analysis with measurements that capture the true deformation values typically within one standard deviation. These results are promising given that the mid-span deformation of the bridge for the given load test is on the scale of only a few millimeters. Future work for this method will also investigate using these results for updating finite element models.
Koopman Operator Based Fault Diagnostic Methods for Mechanical Systems
Traditionally, dynamical systems can be simulated with physics-based model when the design parameters and material property are pre-known. However, when a system is deployed in field and has suffered potential degradation, a physics-based model might be infeasible to obtain. Moreover, the non-linearity and unknown coupling between the system and contacting constraints are often hard to determine accurately. The analysis of those systems becomes practically problematic. In this paper, the Koopman operator is used to learn and represent a dynamic system in a data driven manner. This paper proposes two methods of using the Koopman operator to extract and classify critical parameters of a non-linear dynamic mechanical system for fault diagnosis. The first method proposes a model to extract key features from a dynamic system and feed the features to a neural network to classify the existence of a fault. The second method uses parameters derived from the Koopman operator to create a prediction model with healthy data. This prediction model is then used to predict future system dynamics for a measured time evolution and compare that with direct measurements when future dynamics become available. Both methods are then tested via an experimental case study and the results are discussed.
Unsupervised Condition Monitoring of Structures Using VMD and Isolation Forest
In this paper, an unsupervised condition monitoring of civil infrastructures under environmental and operational variations is proposed. To this end, a couple of the lowest natural frequency signals of the structure recorded over a long period of time are studied for damage. Most of the existing techniques are supervised and require baseline information from the healthy state of the structure. In this paper, however, we explore the possibility of performing unsupervised condition monitoring using Isolation Forest (IF) as a well-known unsupervised machine learning approach. We show that a preprocessing of the frequency signals using the Variational Mode Decomposition (VMD) algorithm plays a key role in the success of the IF algorithm in the unsupervised condition monitoring of structures. To investigate the performance of the proposed method, the benchmark problem of the Z24 bridge is studied in this paper. The results show that the proposed methodology stays successful in most of the cases but at a period of very cold temperature where a nonlinear relationship between the frequencies and temperature presents.
Convolutional Neural Networks for Ultrasonic Guided Wave-Based Structural Damage Detection and Localisation
Among the many methods proposed in the literature to perform structural health monitoring (SHM) of thin-walled structures, two of them appear to be particularly promising and complementary. On the one hand, integrating Machine Learning techniques into this field seems a remarkable solution, since these methods have been shown to be effective in recognising usually hard-to-detect recurring patterns in the measured signals related to the presence of damages in structures, thus improving the diagnostic performances of SHM frameworks. In particular, in the past years, Deep Learning algorithms have gained much importance in this field due to their capability of processing high-dimensional inputs (such as images), thus making it possible to automatically identify onsetting structural damages. On the other hand, ultrasonic guided wave-based approaches are commonly adopted to assess the structural integrity of plate-like structures and pipelines. These approaches, coupled with tomographic algorithms, typically allow performing damage detection and localisation with satisfactory results. However, such reconstruction algorithms are significantly sensors layout-dependent and, as such, they come with some still unsolved issues, leading, for example, to artifacts creation and unsatisfactory tomographic damage localisation performances in case of unevenly distributed network of sensors or when few sensors are installed on the structure. In this work, convolutional neural networks (CNNs) and ultrasonic guided waves are combined into a unique framework, which leverages on the advantages of the two methods to perform damage detection and localisation in platelike structures. Guided waves are excited and sensed by a network of sensors permanently installed on the structure. The information acquired is then converted into grayscale image as is, without performing any prior feature extraction procedure, which is further analysed by a set of CNNs. First, a classifier is employed to perform damage detection. In case damage is identified, the grayscale image is then analysed by two regression CNNs to localise the damage. The framework is tested using experimentally validated numerical simulations of guided waves propagating in a metallic plate available in the literature.
High-Speed Rail Inspection Exploiting an Anomaly Detection Data Processing Approach
Rail internal defects such as detail fracture and transverse fissure are among the leading causes of track-related railway accidents. Therefore, it is critical to develop effective rail defect inspection systems and data processing methods to prevent catastrophic accidents and derailments. This study developed an anomaly detection framework using deep autoencoder (DAE) for rail defect detection. And the team evaluated its performance based on data collected by a prototype passive acoustic rail inspection system. Autoencoder is a semi-supervised learning algorithm that identifies observations in a dataset that deviate significantly from the remaining data. First, the team performed data cleaning and transfer function reconstruction using a dataset collected at the Federal Railroad Administration's Transportation Technology Center in Pueblo, Colorado. Then, handcrafted or knowledge-driven features were extracted from the transfer functions and fed into a statistical outlier analysis as the benchmark. Also, reconstructed transfer functions at clean rail segments were directly used as the input to train and validate the DAE algorithm. The results demonstrated the effectiveness of DAE for structural discontinuity detection and showed promise for rail flaw detection.
Electro-Mechanical Impedance Experiment and Real-Time Data Acquisition for Suborbital Spaceflight
Reusable space vehicles nowadays are regularly used to ferry payloads to space. The structural health monitoring (SHM) systems could be used to further improve safety of the vehicle and reduce operational costs. This contribution describes design, development and implementation of a realtime data acquisition SHM experiment for suborbital spaceflight. The aim of suborbital experiment is to demonstrate successful collection, spatial distribution, on board processing and storage of environmental, structural (SHM) and flight data. In this contribution, details of payload design and operation are provided focusing on SHM application in space environment. Due to space and mass limitation of the payload, a SHM experiment was designed with minimum use of hardware and materials. A miniaturized Canary impedance measurement unit was developed to include the real-time data analysis and communication capabilities as well as a raw data storage on a SD card. A small cantilever beam with an attached piezoelectric sensor was selected as a structural specimen for the in-flight electro-mechanical impedance test. The specimen was modeled analytically and compared to experimental data obtained in laboratory tests. The systems' ability to process impedance data in near-real time was also validated. The results demonstrate ability of the developed SHM system to acquire, store, analyze and communicate the electro-mechanical impedance information to enable a new generation of smart space structures.
On the Application of Topological Data Analysis: A Z24 Bridge Case Study
Topological methods are very rarely used in structural health monitoring (SHM), or indeed in structural dynamics generally, especially when considering the structure and topology of observed data. Topological methods can provide a way of proposing new metrics and methods of scrutinising data, that otherwise may be overlooked. In this work, a method of quantifying the shape of data, via a topic called topological data analysis will be introduced. The main tool within topological data analysis is persistent homology. Persistent homology is a method of quantifying the shape of data over a range of length scales. The required background and a method of computing persistent homology is briefly introduced here. Ideas from topological data analysis are applied to a Z24 Bridge case study, to scrutinise different data partitions, classified by the conditions at which the data were collected. A metric, from topological data analysis, is used to compare between the partitions. The results presented demonstrate that the presence of damage alters the manifold shape more significantly than the effects present from temperature.
A New Approach for Analyzing Safety and Performance Factors in Civil Infrastructures Using Correlation Networks and Population Analysis
Public safety and economic growth are some of the key factors in motivating governments to keep their civil infrastructures, in particular bridges, safe and sound. However, the American Society for Civil Engineers gave a C+ grade for U.S. bridges in 2017. It has been observed that many parameters associated with bridges, such as geographical locations, designs, materials used, and traffic patterns, play key roles in determining the safety and deterioration rates of bridges. However, there is still a lack of studies that analyze the exact impact of all relevant parameters. The motivation of this study is to propose a new data-driven model that employs the concept of population analysis in assessing the impact of each potential parameter and extracting critical information associated with civil infrastructures and their deterioration patterns. We use a correlation network model to analyze and visualize the big data associated with more than 600,000 bridges in the national bridge inventory database. Graph theoretic analysis is applied to the correlation networks to find elements or clusters of interest. A sub-set of 268 bridges across the US of the same age are considered for this case study and the Markov clustering algorithm is used to obtain the clusters from the correlation network. Enrichment analysis is applied to the clusters to identify the significantly enriched input parameters. Preliminary results reveal several facts, including that prestressed concrete bridges in the Southeast region perform better than steel bridges in the Midwestern region. The obtained results are supported by previous research and further validated by the exploratory factor analysis when dividing the clusters into two groups. The proposed network model provides a new data-driven methodology for evaluating the safety and performance of structures. It provides domain experts with valuable information on how to efficiently allocate time and funds for inspecting existing bridges and how to identify key bridge parameters suitable for designing and constructing new bridges in various geographical areas.
A Physics-Guided Deep Learning Approach to Modeling Nonlinear Dynamics: A Case Study of a Bouc-Wen System
In this paper, we study the feasibility of applying the recently proposed Physicsguided Deep Markov Model (PgDMM) [1] for the modeling of hysteretic systems. PgDMM forms a hybrid probabilistic physics-guided framework, bridging physicsbased state space models with Deep Markov Models, thus delivering a hybrid modeling framework for unsupervised learning and identification of nonlinear dynamical systems. Previously, the framework has been tested on nonlinear elastic systems while in this work, we further test the framework on hysteretic systems described by Bouc-Wen type hysteresis. A physics-based model is used, which partially models the systems dynamics, which is implemented by a learning-based term parameterized by a neural network. Our results indicate that the physics-based model essentially enforces a more structured and physically interpretable latent space, which is essential for generalization and prediction capabilities.
Image to Image Translation of Structural Damage Using Generative Adversarial Networks
In the aftermath of earthquakes, structures can become unsafe and hazardous for humans to safely reside. Automated methods that detect structural damage can be invaluable for rapid inspections and faster recovery times. Deep neural networks (DNNs) have proven to be an effective means to classify damaged areas in images of structures but have limited generalizability due to the lack of large and diverse annotated datasets (e.g., variations in building properties like size, shape, color). Given a dataset of paired images of damaged and undamaged structures supervised deep learning methods could be employed, but such paired correspondences of images required for training are exceedingly difficult to acquire. Obtaining a variety of undamaged images, and a smaller set of damaged images is more viable. We present a novel application of deep learning for unpaired image-to-image translation between undamaged and damaged structures as a means of data augmentation to combat the lack of diverse data. Unpaired image-to-image translation is achieved using Cycle Consistent Adversarial Network (CCAN) architectures, which have the capability to translate images while retaining the geometric structure of an image. We explore the capability of the original CCAN architecture, and propose a new architecture for unpaired image-to-image translation (termed Eigen Integrated Generative Adversarial Network or EIGAN) that addresses shortcomings of the original architecture for our application. We create a new unpaired dataset to translate an image between domains of damaged and undamaged structures. The dataset created consists of a set of damaged and undamaged buildings from Mexico City affected by the 2017 Puebla earthquake. Qualitative and quantitative results of the various architectures are presented to better compare the quality of the translated images. A comparison is also done on the performance of DNNs trained to classify damaged structures using generated images. The results demonstrate that targeted image-to-image translation of undamaged to damaged structures is an effective means of data augmentation to improve network performance.
Video Motion Compensation for Fatigue Crack Detection in Steel Structures
Fatigue constitutes a critical limit state affecting the safety of civil infrastructure. Under repetitive loading, structural members are susceptible to fatigue cracking under stresses much lower than the yield strength of the material. In this study, computer vision-based fatigue crack detection using a short video stream taken from a nonstationary camera is presented. Videos taken from a hand-held camera, or an unmanned aerial vehicle (UAV) contain two types of movements: 1) true object movements, and 2) unwanted camera movement due to hand shaking or UAV hovering. In most vision-based structural health monitoring research, feature-based motion compensation techniques are used that require manual selection of fixed objects in the video for feature point selection. Feature point selection from true moving objects in the video could produce inaccuracy in video stabilization. In this study, we propose to use hierarchical model-based motion estimation for global motion compensation, which does not require manual selection of fixed objects. First, we construct a pyramid of target and reference images and then estimate motion from top to bottom of the pyramid while accumulating the geometric transformation, by which the camera movement can be removed. Then, we detect salient feature points in the region of interest and track the motion of feature points throughout the video using the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm. Subsequently, a crack detection, and localization algorithm is applied to search for differential point movements caused by fatigue crack opening and closing. To evaluate effectiveness of the proposed method, a laboratory experiment was conducted on a C(T) specimen with an in-plane fatigue crack. Results show that proposed method was able to effectively compensate the camera motion and detect the presence of the fatigue crack.
Nonlinear Reduced Order Modelling of Soil Structure Interaction Effects via LSTM and Autoencoder Neural Networks
In the field of structural health monitoring (SHM), inverse problems which require repeated analyses are common. With the increase in the use of nonlinear models, the development of nonlinear reduced order modelling techniques is of paramount interest. Of considerable research interest, is the use of flexible and scalable machine learning methods which can learn to approximate the behaviour of nonlinear dynamic systems using input and output data. One such nonlinear system of interest, in the context of wind turbine structures, is the soil structure interaction (SSI) problem. Soil demonstrates strongly nonlinear behaviour with regards to its restoring force and has been shown to considerably influence the dynamic response of wind turbine structures. In this work, we demonstrate the application of a recently developed nonlinear reduced order modelling method, which leverages Autoencoder and LSTM neural networks, to a nonlinear soil structure interaction problem of a wind turbine monopile subject to realistic loading at the seabed level. The accuracy and efficiency of the methodology is compared to full order simulations carried out using Abaqus. The ROM was shown to have good fidelity and a considerable reduction in computational time for the system considered.
Multi-View Deep Learning for Reliable Post-Disaster Damage Classification
This study aims to enable more reliable automated post-disaster building damage classification using artificial intelligence (AI) and multi-view imagery. The current practices and research efforts in adopting AI for post-disaster damage assessment are generally (a) qualitative, lacking refined classification of building damage levels based on standard damage scales, and (b) trained based on aerial or satellite imagery with limited views, which, although indicative, are not completely descriptive of the damage scale. To enable more accurate and reliable automated quantification of damage levels, the present study proposes the use of more comprehensive visual data in the form of multiple ground and aerial views of the buildings. To have such a spatially-aware damage prediction model, a Multi-view Convolution Neural Network (MV-CNN) architecture is used that combines the information from different views of a damaged building. This spatial 3D context damage information will result in more accurate identification of damages and reliable quantification of damage levels. The proposed model is trained and validated on reconnaissance visual dataset containing expertlabeled, geotagged images of the inspected buildings following hurricane Harvey. The developed model demonstrates reasonably good accuracy in predicting the damage levels and can be used to support more informed and reliable AI-assisted disaster management practices.
Does the Curse of Dimensionality Apply to Unsupervised SHM? Investigating the Trade-Off Between Loss of Information and Generalizability to Unseen Structural Conditions
The curse of dimensionality (CD) brings difficulties in pattern recognition problems, such as those found in structural health monitoring (SHM). Dimensionality reduction techniques (DR) make data more manageable by reducing noise and noninformative portions. There exists a trade-off between CD and the loss of information due to the application of DR. Even though in supervised SHM, DR techniques are shown to be effective, for unsupervised SHM, the trade-off must be assessed due to the unknown data population of novel classes. This study assesses the trade-off concerning a novel method working with a raw frequency-domain feature, the fast Fourier transform (FFT). Different DR techniques are applied to the initial FFT-based feature to assess the trade-off, and detection results are compared. The results indicate that the loss of information can have detrimental effects, such as lowering the detection accuracy by 60% for the autoencoder-based DR. The accuracy reduction is present for all different DR techniques applied in the study; however, regularization lessens the accuracy decrements. This phenomenon indicates the assumption that novelties show themselves in less-vary portions of the baseline condition to be not true.
The Propagation Characteristics of Leaky Lamb Wave and Application for Resin Impregnation Monitoring
The leaky Lamb wave has a broad application prospect in the fields of biosensing, concrete construction, composite material manufacturing, and so on. This paper first theoretically investigates the propagation characteristics of leaky Lamb waves when the rigid mold is loaded with water or viscous resin by one side in the semi-infinite space. As the thickness of the liquid increases, more and more energy leaks into the liquid through the solid-liquid interface. There is more energy leaking into the viscous resin than that in the water. The phase velocity and energy velocity decrease as the liquid increases. Then the amplitude reduction and phase delay of A0 mode due to the liquid loading from theoretical analysis were verified by subsequent experiments. One valid application of leaky Lamb waves in resin impregnation monitoring during the Vacuum Assisted Resin Infusion of composite materials manufacturing process was also investigated experimentally.
Debonding Analysis of Composite Material Using Ultrasonic Wave-Based NDT Methods
The article investigates multiple debonding in a glass fibre reinforced polymer structure (GFRPS) using nondestructive testing (NDT) based on ultrasonic guided waves (UGW) propagation. The piezoelectric transducers (PZT) attached to the material excite the UGW and the registered time signals are analyzed. The debonding are in various depths of the GFRPS. It was assessed using NDT-based tools. The presence of debonding and wave scattering based on the depth and location in GFRPS is studied. This is followed by using signal processing methods to visualize and analyze the different characteristics of the ultrasonic waves before and after the debonding. Thus an experimental-based approach to identify the debonding inside the GFRPS and its influence is studied.
Spatial Filtering Technique-Based Enhancement of the Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID)
A common active structural health monitoring (SHM) solution for thin-walled structures consists of processing ultrasonic guided waves signals, which are excited and sensed by means of a network of piezoelectric devices installed on the structure, with the purpose of providing a tomographic reconstruction-based damage probability map of the structure. A promising reconstruction algorithm typically employed within this framework is the Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID) algorithm, which has been shown to provide satisfactory results in terms of damage detection and localisation. However, this algorithm comes with some disadvantages and minor issues, such as artefacts creation in case an unevenly distributed sensors layout is installed on the structure, which may significantly worsen the damage diagnosis performance of the monitoring framework. In this paper, an enhancement of the original RAPID algorithm is presented, which exploits spatial filtering techniques to reduce possible artificially created artefacts, thus allowing installing on structures any network of sensors without reducing the diagnostic performances. The improved damage localisation accuracy obtained using the proposed algorithm is proven by means of a case study involving a numerical model of a realistic composite panel with an unevenly distributed network of sensors.
A Parametrized Reduced Order Model for Rapid Evaluation of Flaws in Guided Wave Testing
Data from guided wave propagation in structures, produced by piezoelectric elements, can offer valuable information regarding the possible existence of flaws. Numerical models can be used to complement the attained data for refining the potential for flaw characterization. Unfortunately, evaluation of these models remains computationally expensive, especially for small defects, due to the short wavelength required for detection and, the in turn fine discretization in time and space. This renders realtime simulation infeasible, rendering GWapproaches less attractive for inverse problem formulations, where the forward problem needs to be solved several times. We propose an accelerated computation method, which exploits the properties of guided waves interacting with defects, where an extra band of waves is created, whose phase is differentiated, depending on the location of the flaw (e.g. notch) within the medium. To expedite the actual simulation for the inverse problem, the system is parametrized in terms of the location of the flaw and, in an offline phase, is repeatedly solved to produce snapshots of the systems response. The snapshots are used to create a physics informed interpolation of the solution of the wave propagation problem for different flaw locations. The gained information is then used in an inverse setting for localising the defect using an evolution strategy as a means to stochastic, derivative-free numerical optimization. The method is demonstrated in simulations of a 2D slice of a thin plate.
Comparison of Plate and Solid Spectral Element Modeling of Composite Delamination for Guided Wave Simulations
As composite laminates have become essential to numerous modern industries, Structural Health Monitoring (SHM) strategies have to be implemented in order to manage the aging of structures made out of composite materials. In this paper, we present and compare two approaches involving solid and plate Spectral Elements (SE) in the time domain, with respect to their ability of modeling composite delamination: a task which lies in the context of modelbased data processing for non destructive evaluation techniques with Guided Waves (GWs). To this end, we review the multilayer flat shell delamination model [1], and propose a singlelayer alternative by extending the Layered Solid Spectral Element (LSSE) [2] method by means of partition of unity enrichment functions [3].These approaches are then compared in a numerical example with different damage scenarios. Finally, conclusions on the benefits and drawbacks of these two strategies are presented.
On the Existence of Zero Group Velocity Modes in Rails
Ultrasonic guided waves are of practical interests for nondestructive evaluation (NDE) and structural health monitoring (SHM) since users can promote desirable wave modes for damage detection, thickness measurement, surface condition characterization, stress measurement, and so on. This study focuses on demonstrating the existence of zero group velocity (ZGV) modes for guided waves in free rails. First, the team computed dispersion curves of AREMA standard rails to identify ZGV points through semi-analytical finite element analysis (SAFE). Second, finite element models were established to spatially sample wave propagation in free rails for wavenumberfrequency domain analysis. The results of finite element simulations were compared with dispersion curves produced by SAFE, and multiple points were identified with vanishing group velocity at non-zero wavenumbers. And resonances with positive and negative wavenumbers revealed that the observed standing waves phenomenon results from the interference of two traveling waves propagating with opposite directions. Our observation and developed methodology have potential applications for rail defect detection, support condition assessment, and rail stress measurement.
Using LiDAR to Identify Planar Measurement Regions in Ultrasonic Inspections of Complex Structures
Acoustic Steady State Excitation Spatial Spectroscopy (ASSESS) is an ultrasonic inspection technique that was developed to rapidly evaluate large structures and identify regions of damage. An ultrasonic transducer affixed to the structure emits a single tone, and a scanning laser Doppler vibrometer (LDV) records the structures steady-state surface velocity response. Previous work has shown how local wavenumber can be estimated from the complex steady-state velocity response. This process has proved successful in detecting corrosion defects, delaminations, and regions of varying thickness. This work introduces a new processing method that utilizes a LiDAR generated point cloud representation of the scan region to identify and extract large planar sections from the measurement without causing distortion in the final wavenumber estimates. This new method uses the RANSAC algorithm to robustly extract planar sections and maps the complex steady-state response data onto a uniform grid on the detected planes. This is done in order to facilitate the use of an existing wavenumber estimation technique. We present wavefield and wavenumber results generated by applying this algorithm on a real-world dataset from a large area scan in an industrial structure with steel walls containing stringers, columns, and regions with different thicknesses.
Mesoscale Homogenization Numerical Study on the Significance of Concrete Mesoscale Structure on Wave Propagation of Rectangular RCFSTs with Debonding
In this paper, in order to efficiently distinguish the influence of both interface debonding defect and the mesoscale structure of concrete core on the stress wave field and the response of an embedded Piezoelectric-lead-zirconate-titanate (PZT) sensor in rectangular concrete filled steel tube (RCFST) members, a two dimensional (2D) mesoscale numerical concrete with homogenization approach considering the random distribution of circle, ellipse and irregular polygon aggregates is proposed firstly. Then, mesoscale homogenization simulation on stress wave fields within the cross-sections of RCFST members with and without interface debonding defects are carried out, respectively. The effect of both mesoscale structure of concrete core and the interface debonding defect on the stress wave field of each member is discussed. Therefore, the time-domain response of the embedded PZT sensor in the RCFST members coupled with PZT patches under sweep frequency excitation signal is determined and compared when both mesoscale models and their homogenization models are used. The sensitivity of the wavelet packet energy of the embedded PZT sensor response on the variation of both mesoscale structure of concrete core and the dimension of interface debonding defects is investigated. The detectability of interface debonding using stress wave measurement is illustrated efficiently with the proposed mesoscale homogenization modelling approach even the mesoscale structure of the concrete core is considered.
Instance-Segmentation-Based Dense On-Site Rock Fragment Recognition During Real-World Tunnel Excavation
Timely recognition of rock fragments and their morphological sizes can help adjust excavation parameters during tunnel boring machine (TBM) tunneling. Traditional manual inspection highly relies on experiences and subjective judgments of human operators and conducting sieving tests is not real-time and energy-consuming. Rock fragments in real-world images are often observed against a dark background, distributed with high size diversity, complicatedly distributed, and blocked by each other. To solve these problems, this study proposes a novel instance segmentation-based method for on-site rock fragments recognition. The proposed instance segmentation model includes two subnetworks: object detection and semantic segmentation. The results show that 88% of rock fragments can be recognized, and the average recall and average IoU values reach 0.85 and 0.75 on the 15 test images, respectively. Besides, both small and large rock fragments can be recognized well. The predicted size distributions of the major and minor axis lengths of the rock fragments fit well with the ground-truth ones statistically. In conclusion, this study can provide both visual recognition and statistical results for the size distribution of on-site rock fragments.
Machine Learning of Ultrasonic Data for Expansion Prediction of Concrete with Alkali-Silica Reaction
Ultrasonic nondestructive testing is a promising method for performing damage assessments on concrete subjected to alkali-silica reactions (ASRs). Previous research incorporated only some ultrasonic wave parameters, and the other information from the ultrasonic signals was discarded. In this work, 13 features, including wave velocity and wavelet features, were extracted from the ultrasonic signals. A curve-fitting method was used to fit a polynomial relationship between the wave velocity and expansion of one concrete sample subjected to ASR to predict the expansion of another concrete sample subjected to ASR. Support vector regression (SVR), a machine learning model, was trained using all 13 features derived from the ultrasonic data obtained from the ASR samples. The SVR was then tested using the datasets from the ASR-2D sample. The performance showed that the curve-fitting method and the SVR had poor prediction results on the expansion of the ASR-2D sample. With feature selection, the performance of the SVR model using six selected features was significantly improved.
Incorporating Pre-Existing Cracks in Structural Assessment of RC Structures
Cracks in reinforced concrete (RC) structures can be detrimental as they grow beyond the limits. Cracks should be included in structural assessment methods to ensure the durability and load capacity of existing structures. However, conventional models used in the assessment of existing structures do not reflect the real cracking condition which implies that advanced assessment methods are required. In this study, pre-existing cracks were introduced into finite element analysis to identify the ductility, failure characteristics, and ultimate capacity of cracked structures. A beam specimen taken from the edge beams of an existing bridge had been subjected to a four-point bending test, and the results are used in this study for validation purposes. The specimens showed varying levels of cracking due to loading as well as reinforcement corrosion during the service life. Five different analyses were carried out to account for the effect of loading cracks and corrosion cracks based on two crack modeling approaches, namely weakened element approach and weakened bond-slip relation approach. The results showed that the failure of the beams was caused by anchorage failure. The differences in the load capacity predicated by different models are discussed. It was observed that incorporating pre-existing cracks by using weakened elements and weakened bond-slip relation approaches can be a practical method to model and assess cracked RC beams.
Kalix Bridge Digital Twin Structural Loads from Future Extreme Climate Events
Environmental loads, such as wind and river flow, play an essential role in the structural design and structural assessment of long-span bridges. Climate change and extreme climatic events are threats to the reliability and safety of the transport network. This has led to a growing demand for digital twin models to investigate the resilience of bridges under extreme climate conditions. Kalix bridge, constructed over the Kalix river in Sweden in 1956, is used as a testbed in this context. The bridge structure, made of posttensioned concrete, consists of five spans, with the longest one being 94 m. In this study, aerodynamic characteristics and extreme values of numerical wind simulation such as surface pressure are obtained by using Spalart-Allmaras Delayed Detached Eddy Simulation (DDES) as a hybrid RANS-LES turbulence approach which is both practical and computationally efficient for near-wall mesh density imposed by the LES method. Surface wind pressure is obtained for three extreme climate scenarios, including extreme windy weather, extremely cold weather, and design value for a 3000-year return period. The result indicates significant differences in surface wind pressure due to time layers coming from transient wind flow simulation. In order to assess the structural performance under the critical wind scenario, the highest value of surface pressure for each scenario is considered. Also, a hydrodynamic study is conducted on the bridge pillars, in which the river flow is simulated using the VOF method, and the water movement process around the pillars is examined transiently and at different times. The surface pressure applied by the river flow with the highest recorded volumetric flow is calculated on each of the pier surfaces. In simulating the river flow, information and weather conditions recorded in the past periods have been used. The results show that the surface pressure at the time when the river flow hit the pillars is much higher than in subsequent times. This amount of pressure can be used as a critical load in fluid-structure interaction (FSI) calculations. Finally, for both sections, the wind surface pressure, the velocity field with respect to auxiliary probe lines, the water circumferential motion contours around the pillars, and the pressure diagram on them are reported in different timesteps.
Numerical Studies on Bridge Inspection Using Data Obtained from Sensors on Vehicle
In this study, we proposed a methodology for a technique to simultaneously estimate the mechanical parameters of vehicles and bridges and road surface roughness from vehicle vibration. The MCMC (Markov chain Monte Carlo) method was used to search for parameters from vehicle vibrations generated by numerical simulation. The results obtained are estimable even in the presence of bridge stiffness reduction, which suggests the possibility of bridge damage detection using vehicle vibration.
An Image-Based Concrete Crack Detection Method Using Convolutional Neural Networks
This paper proposes a CNN-based crack detection method that can recognize and extract cracks from photos of concrete structures. The algorithm consists of two subsequent procedures, classification, and segmentation, achieved by two convolutional neural networks respectively. First, full images are divided into patches and classified as positive and negative. Then, those sub-images classified as positive are further processed by the image segmentation procedure to obtain the pixel level geometry of the cracks. For the classification part, the performance of transfer learning models based on pre-trained VGG16, Inception V3, MobileNet and DenseNet169 is compared with different classifier. Finally, the CNN based on MobileNet was trained with 30,000 training images and reached 97% testing accuracy and 0.96 F1 score on testing image. For the segmentation part, different neural networks based on the elegant U-net architecture are built and tested. The models are trained with 3840 crack images and annotated ground truth and compared quantitatively and qualitatively. The model with the best performance reached 88% sensitivity on test data set. The combination of the classification and segmentation neural networks achieves an image-based crack detection method with high efficiency and accuracy. The algorithm can process any full image size as input. Compared with most machine learning based crack detection algorithms using sub-image classification, a relatively larger patch size is used in this paper and in this way the classification is more robust and accurate. On the other hand, the negative areas in the full image will not be concerned in the segmentation procedure and this fact not only saves a lot of computational power but also significantly increases the accuracy compared to the segmentation performed on full images.
A Physics Informed Neural Network Integrated Digital Twin for Monitoring of the Bridges
In recent years the Digital Twin (DT) paradigm has been studied as a futuristic tool for the next generation of infrastructures. Due to the interdisciplinary nature of the design, construction, monitoring, and maintenance of the infrastructures and the cooperation of several stakeholders throughout their lifetime, it is indispensable to introduce a comprehensive platform for the digital representation of infrastructures. Although the DT emphasizes the role of digital modeling and data analysis, there is a gap between physical modeling and data-driven tools. The newly introduced Physics Informed Neural Networks (PINNs) are capable of not only filling this gap but also representing a unified real-time platform for different users from various fields. These algorithms suggest an agile environment for users to introduce different criteria from the design stage to the health monitoring period. The PINN integrates both physical modeling and data analysis in a unique algorithm, helping them interact simultaneously and providing real-time, reliable responses. By means of the PINN, the DT can learn and update the model from various data sources with a unique platform, which plays an essential role in the rapid flow of information and transparency of data-based calculations. The dynamic ambiance of the PINN enables the users to interact with the modeling procedure and track the analysis. In this study, the details of the proposed platform for the integration of the PINNs in the DT are addressed for monitoring the bridges. Extensive numerical studies are provided for various scenarios of sensor equipment, including sensor type, data accuracy, and installation pattern. The performance of the proposed platform is evaluated for predicting subsequent responses to ensure the reliability of the responses in future decision makings.
Sensor Fault Diagnosis Coupling Deep Learning and Wavelet Transforms
Sensor networks facilitate collecting measurement data necessary for decision making regarding structural maintenance and rehabilitation in structural health monitoring (SHM) systems. Nevertheless, the reliability of decision making in SHM systems depends on the proper operation of the sensors. Sensors may exhibit faults, entailing faulty data and incorrect judgment of structural conditions. Therefore, fault diagnosis (FD), comprising detection, isolation, identification, and accommodation of sensor faults, has been introduced in SHM systems, enabling timely detection of faulty data while advancing reliable operation of SHM systems. Traditional FD approaches based on analytical redundancy take advantage of correlated sensor data inherent to the SHM system, sometimes neglecting the fault identification step, and are implemented for specific sensor data. In this paper, an analytical redundancy FD approach for SHM systems, coupled with machine learning algorithms and wavelet transforms, capable of processing any type of sensor data is presented. A machine learning (ML) regression algorithm is proposed for fault detection, fault isolation, and fault accommodation, and an ML classification algorithm is proposed for fault identification. Continuous wavelet transform (CWT) is used as a preprocessing step of fault identification, exposing fault patterns in the data. The ML-CWT-FD approach is validated using data from a real-world SHM system in operation at a railway bridge implementing a deep neural network as ML regression algorithm and a convolutional neural network as ML classification algorithm. As a result of this paper, the ML-CWTFD approach is demonstrated to be capable of ensuring reliable SHM systems.
NDE Data Correlation Using Encode-Decoder Networks with Scalogram Images
Nondestructive Evaluation (NDE) technologies are increasingly used for structural condition assessments. Over the lifespan of a structure, a variety of NDE techniques may be employed, leading to a scenario where a structures life-cycle time history is depicted through a variety of complex and heterogeneous measurements. Therefore, improved understanding of the statistical associations between NDE data sources would allow engineers to integrate these data sources for analysis purposes. It would also provide new insights into the fundamental information shared between heterogeneous NDE observations, potentially leading to new forms of structural monitoring and assessment. This paper explores the correlations between NDE data types through an encoder-decoder neural network architecture. The network is designed to take in one type of NDE measurement as input, generating a synthetic measurement from a second NDE measurement as output. At the center of the encoder is a dimensionally reduced latent representation of the information that is shared between two associated NDE data sources. Additionally, this paper shows how transforming waveform NDE data into 2D time-frequency images using a Continuous Wavelet Transform (CWT) facilitates network training and representation of these shared fundamental data features. To illustrate this concept, the results from a series of laboratory scale tests are presented, representing how this network architecture would represent information collected from NDE of bridge decks.
An Autonomous Bridge Load Rating Framework Using Digital Twin
Load rating of bridges is used to understand the working status and carrying capacity of bridge structures and components and is necessary to the safety of transportation. The current manual load rating procedure is, however, time-consuming. An intelligent and automatic load rating approach can be beneficial to supplement or eventually perhaps replace the current manual procedures. The innovation of this paper lies in developing an autonomous load rating framework by leveraging Digital Twin (DT) techniques. Full-scale laboratory testing of a bridge slab was conducted to verify the efficiency of the proposed framework. The ultimate moment capacity of the slab was obtained by carrying out four-point bending test. The testing procedure was monitored in real-time with multiple strain gauges. A real-scale finite element model of the slab was developed and calibrated with the testing results. The proposed DT framework of the bridge slabs was developed by integrating the numerical modeling and the strain monitoring. The proposed DT framework is intended for field application, and field results will be discussed.
Active and Passive Monitoring of Rail Through the Application of Machine Learning Algorithm
Non-destructive testing of rail is an essential part of maintaining in-service rail tracks to avoid accidents. Conventional methods such as the traditional ultrasonic technique are relatively slow and cumbersome resulting in non-frequent monitoring. This study explores active and passive techniques for continuous and long range rail damage monitoring. Firstly, the experiment, simulation and challenges of the ultrasonic guided wave generated through surface-bonded piezoelectric transducer are studied. Due to the presence of numerable inseparable modes occurring in rail, the application of machine learning algorithms is explored. Classification of damage in rail head and severity of damage have been achieved using features derived from the signal. To map changes in features with respect to damage, various ML algorithms are trained, tested and compared. Among them, the k-nearest neighbour has been found to have the highest accuracy in classifying rail head damage, while the Gaussian process regression is best suited for determining damage severity. Trained algorithms are then tested with simulated and experiment of different damage sizes. Secondly, the application of acoustic emission in rail is investigated through simulation and pencil lead break source experiments. The behaviour of rail as waveguide and wide band of generating frequency are observed to be the challenges in determining the zone of AE source. Thus, to classify the zone of AE source, a deep learning algorithm based on continuous wavelet transform is presented. This method results in 88% accuracy in finding the AE source zone. The presented study then concluded with challenges in monitoring complex geometry such as rail and application of machine learning in monitoring.
Probabilistic Prediction of Anaerobic Reactor Performance Using Bayesian Long Short-Term Memory Artificial Recurrent Neural Network Model
Engineering structures are designed and managed for safe operations and to meet defined levels of service. The floating cover on the wastewater treatment lagoon at Melbourne Waters Western Treatment Plant (WTP), in Werribee, is a critical asset whose primary functions are to allow for the anaerobic breakdown of organic matter in the raw sewage and to collect the biogas that is released during this process. Whilst our work initially focused on the development of asset-based diagnostic capabilities to understand how the floating cover respond to the prevailing physical conditions [1-3], coupling these diagnostic capabilities with service-based performance requirements results in new and different imperatives for the structural health and performance monitoring of this engineering structure. This paper reports on the development of an element of this new monitoring strategy and a machine learning capability to predict the biogas collection rates based on past operational decisions and conditions. The study presents a Bayesian Long Short-Term Memory neural network model and investigates its effectiveness for the probabilistic prediction of biogas collection rates at WTP. The probabilistic approach is based on a Gaussian distribution output layer and Monte-Carlo dropout method to estimate the aleatoric and epistemic uncertainties, respectively. The data pre-processing and optimisation of the neural network model are reported. The findings also indicate using a dropout probability beyond 40% adversely prevents learning of complex patterns in the data and overly regularises the network model prediction. The study serves as a fundamental basis in implementing machine learning to transition high-value assets into smarter structures with diagnostic and prognostic capabilities by providing an example where service requirements are also considered.
UAS Inspection Image Enhancement Coupled with Denoise Algorithm Based on Deep Neural Network
Unmanned Aerial System (UAS) technologies integrated with image processing algorithms are considered timely and useful for bridge inspections because of improved accessibility, recording ability, and cost-efficiency compared to the conventional inspection approach. The image processing algorithms can improve the ability of the UAS-aided bridge inspections in efficiently identifying and quantifying deterioration. This study was aimed to inspect an in-service single-span precast concrete bridge on a rural roadway in South Dakota using UAS technologies coupled with a Deep Neural Network (DNN) denoise algorithm. During the inspections, Phantom 4 and DJI Matrice 210 UASs recorded several videos for different bridge elements (e.g., girders and decking), and a total of 21,784 inspection images were extracted from the videos with a duration of more than 14 minutes. Deteriorations specific to the bridge elements such as spalling and rust were characterized by performing the DNN-aided image processing algorithm with the extracted inspection images. The DNN allowed for computation and analysis between input and output image data to reduce the noises on the images. Besides, a grayscale image enhancement algorithm was considered to improve the visibility of images by optimizing image contrast settings. With the visibility-improved images, detailed quantification on the detected deterioration per bridge element was carried out using a pixel-based measurement tool. Based upon the studys results, it was revealed that the UAS technologies with the DNN denoise algorithm were able to successfully characterize and quantify visible deteriorations to the certain bridge elements using pixel-based tools.
Use of Deep Encoder-Decoder Network for Sub-Surface Inspection and Evaluation of Bridge Decks
The automation of various processes underlying maintenance and inspection of bridges using different robots have gained considerable attention in recent literature. For the development of effective methods to automate existing manual processes, a number of different solutions have been proposed. In this paper, the automation of rebar detection and localization will be discussed, which is one of the process for sub-surface health inspection of bridges. This study explores the utilization of Deep Encoder- Decoder Networks for the segmentation of GPR data in the form of B-scan images to extract parabolic rebar profiles. This research area is problematic, as the B-scan image data is fraught with noise, signal reflection and other artefacts that hinder the effective extraction of these rebar profiles. The data is collected in this study using Ground Penetrating Radar (GPR) sensor, which is employed in this study consist of data from 8 different bridges from different parts of the United States. A leave-one-out approach was used for the training and validation of the performance of the proposed system; the data from seven bridges was used for training and validation was performed on the remaining single bridge data. A number of different encoder modules have been trained and evaluated using SegNet as the backbone architecture. The performance of the proposed rebar detection and localization system has been evaluated in terms of different qualitative and quantitative metrics. On average, for the different encoder modules, the mean intersection-over-union (mIOU) values range between 60%-70%. The qualitative examination has highlighted the level of similarity between the ground truth and outputs from the different encoder modules within the SegNet framework.
Steel Defect Detection in Bridges Using Deep Encoder-Decoder Networks
Recent major accidents related to bridges have emphasized the need for developing effective technological solutions for defect detection, which can minimize the possibility of bridge-related accidents in the future. In this respect, this research will focus towards development of automated system for the detection of defective regions within different steel parts of bridges. At present, there is no open-source image dataset, which can be used for this purpose. Consequently, the training dataset has been developed by using images acquired from bridges in Vietnam and validation was performed using images acquired from Lovelock bridge situated at Highway-80, Lovelock, NV, USA. A total of 5,500 (4,000 images for training and 1,500 for validation) images of different dimensions have been used the original dimensions of the steel bridge images have been modified 572 × 572 pixels, which have been used for the training and evaluation of the dataset on different Deep Encoder-Decoder networks. The use of diverse data from different bridges will allow the development of a robust Deep Encoder-Decoder network with considerable implications for practical systems in the future. This study will employ state-of-the-art Deep Encoder-Decoder network, which have been recently developed for other applications. However, no such study has been developed for defect detection in steel bridges. A comparative evaluation of different Deep Encoder-Decoder networks will be examined. At the same time, the performance of the system will be compared with recent advanced approaches. The results reveal the considerable potential of Deep Encoder-Decoder towards defect detection of steel bridges, which will be further exploited in the future studies.
Combined Lidar and Sonar Mapping for Partially Submerged Infrastructure
Advances in robotic mapping, planning, and perception have spurred applications-based robotics research in the domain of infrastructure inspection and preservation. Though a significant portion of this research has centered around the use of unmanned aerial, ground, and underwater vehicles, research in the use of unmanned surface vehicles (USVs) is limited. USVs present a unique opportunity to capture combined maps above and below water, which is essential for the inspection of waterspanning bridges, harbors, dams, and levees. In this paper, we investigate the use of USVs for infrastructure inspection by outfitting a USV platform with a multibeam sonar, horizontally and vertically mounted lidars, several ruggedized RGB cameras, and a high-rate inertial measurement unit (IMU). By time-synchronizing all sensors, we are able to fuse information collected from lidar, camera, and IMU units via tightly-coupled lidar-visual-inertial (LVI) simultaneous mapping and localization (SLAM). We validate our methodology by collecting sensory data of an abandoned quarry and by generating a combined 3D point cloud map using lidar data, multibeam sonar data, and maximum a posteriori trajectory from the LVI SLAM approach. Experiments validate the performance of the proposed USV system, highlighting challenges in extrinsic calibration of non-overlapping sensors, sonar denoising, and refined inter-keyframe pose estimation for key-frame based SLAM approaches.
Design of a Robotic Taping Mechanism for UAV-Based Wind Turbine Blade Maintenance
Wind energy generated through wind turbines is a critical contributor towards realizing a renewable energy economy. Maximizing the efficiency of power generation from wind turbines is required to meet target generation capacities. The leading edge of wind turbines blade is designed such that its smooth, aerodynamic surface will produce maximum power given the size specification of the turbine. As these blades age, they typically suffer from leading edge erosion, or LEE, which is the gradual erosion of the blades leading edge. Not only does LEE shorten a blades lifespan, but it also negatively affects performance, reducing annual energy production. Application of wind blade protection tape is a frequently used solution for LEE on the damaged area. Tape application requires a crew of technicians with a lift and is considered high-risk where one mistake can lead to fatal injury. The focus of this paper is to present a method of automating the wind blade protection tape application by using a UAV with an endeffector. Specifically, the end-effector is an automatic taping mechanism that will apply wind blade protection tape to the damaged area. The paper discusses the overall robotic arm design, topology optimization, and hardware components used to create the operation for the automatic taping mechanism. The end-effector is designed to dispense tape, extend to create contact to the surface, and cut to finish the application. The rest of the arm is used for the motion of tape application vertically starting from the bottom of the damaged region. The process is completed once the damaged area has been covered with protection tape and tape has been cut. The developed end-effector demonstrates the effectiveness of the taping operation, ultimately conserving the wind blades lifespan and decreasing the risk of human injury.
The Ceiling Effect and Flight Insight of Unmanned Aerial Vehicles During Proximity Inspection of Bridges via Computational Fluid Dynamics Modeling and Simulations
UAVs face some critical challenges in pervasive applications, such as navigation in GPS-denying areas, obstacle avoidance, and tradeoff between payload and flight time. The INSPIRE UTC at Missouri S&T is developing a clamping UAV, a hybrid flying and traversing robot, to make it possible that the UAV flies under a bridge girder, grabs the girder, and moves along the girder for inspection. This heavy UAV is difficult to control underneath the deck when equipped with multiple cameras. In this study, CFD modeling and simulation are conducted to understand the flight behavior and ceiling effect of the UAV under the bridge deck. First, 3D scanning and reverse engineering are used to build CFD models. Second, a series of single propeller tests are conducted to validate the process of CFD modeling and the simulation results. Third, based on the workflow and technique validated through single propeller CFD analysis, the CFD model of an entire UAV is established and analyzed to predict the behavior of the UAV and understand its interaction with upper boundaries as it approaches the bridge ceiling vertically. The CFD simulation results show that the ceiling effect of the designed clamping UAV is insignificant when the UAV approaches the standard bridge deck with enough depth. These results provide a technical reference for the design and control of the clamping UAV for bridge inspection.
Robotic Inspection and Characterization of Subsurface Defects on Concrete Structures Using Impact Sounding
Impact-sounding (IS) and impact-echo (IE) are well-developed non-destructive evaluation (NDE) methods that are widely used for inspections of concrete structures to ensure the safety and sustainability. However, it is a tedious work to collect IS and IE data along grid lines covering a large target area for characterization of subsurface defects. On the other hand, data processing is very complicated that requires domain experts to interpret the results. To address the above problems, we present a novel robotic inspection system named as Impact-Rover to automate the data collection process and introduce data analytics software to visualize the inspection result allowing regular non-professional people to understand. The system consists of three modules: 1) a robotic platform with vertical mobility to collect IS and IE data in hard-to-reach locations, 2) vision-based positioning module that fuses the RGB-D camera, IMU and wheel encoder to estimate the 6-DOF pose of the robot, 3) a data analytics software module for processing the IS data to generate defect maps. The Impact-Rover hosts both IE and IS devices on a sliding mechanism and can perform move-stop-sample operations to collect multiple IS and IE data at adjustable spacing. The robot takes samples much faster than the manual data collection method because it automatically takes the multiple measurements along a straight line and records the locations. This paper focuses on reporting experimental results on IS. We calculate features and use unsupervised learning methods for analyzing the data. By combining the pose generated by our vision-based localization module and the position of the head of the sliding mechanism we can generate maps of possible defects. The results on concrete slabs demonstrate that our impact-sounding system can effectively reveal shallow defects.
A Note on Axioms of Structural Health Monitoring
In this paper, the axioms of Structural Health Monitoring (SHM) presented by Worden et al. [2007] are discussed in detail. In many cases, the intent of the axioms is found to be correct, but the terminologies used are confusing. Also, it was found that some axioms could be derived from other axioms that were given in the paper. Based on the discussion presented in this paper, it is suggested to replace the seven axioms given by Worden et al. [2007] with a set of three new axioms. Counter-examples are presented to dispute the axioms where necessary. Similar to Worden et al. [2007], the term axiom is used outside its meaning in the field of mathematics and logic. In both the papers, the term axiom refers to the fundamental truths, which cannot be contradicted in the field of SHM.
New Excitation (Multiple Width Pulse Excitation (MWPE)) Method for SHM Systems Part 1: Visualization of Time- Frequency Domain Characteristics
Structural health monitoring (SHM) of additively manufactured polymer parts is challenging due to the very strong attenuation of the surface waves. To excite the part surface at a very wide frequency band in a very short time, Multiple Width Pulse Excitation (MWPE) signal was introduced. MPWE was used to excite the surface of the structure for the implementation of the Surface Response to Excitation (SuRE) method. A cross-shaped polymer part was fabricated additively for the identification of the hidden geometry of the infill. The part had four extensions with identical geometry but different internal designs. Two of the extensions had cross infills and the other two had square infills. For each type of infill, one extension had 1 mm and the other extension had 2 mm thick skin. The part was excited at the middle with WMPE excitation and the dynamic response was monitored at the end of each extension. The Short-Time Fast Fourier Transform (STFFT) was used for the analysis of the signal in the time-frequency domain. The two dimentional sum of the squares of the differences (2DSSD) was used for the classification of the signal. Compressive force and type of infill was identified accurately for all the test cases.
On Implementing an Irreducible Element Model Schema for Population-Based Structural Health Monitoring
This paper is the second in a series in which the aim is to provide an underlying database technology for enabling the user interaction required for Population-Based Structural Health Monitoring (PBSHM). In the first paper in the series, the groundwork was laid for a PBSHM Schema which enabled the storage of channel data via a Time First approach. PBSHM considers grouping similar structures together to gain additional insights from the group, compared to a single entity. Part of the PBSHM process is being able to identify which structures, or substructures, are similar. To enable this a standardised method of representing each structure must be used; here, an Irreducible Element (IE) model is employed. This paper builds on the groundwork that has been laid in the creation of IE models and defines a standardised format and properties for an IE modal to enable graph matching algorithms to find similar structures. The standardised format has been implemented via an IE-model Schema within the PBSHM Schema.
Minimizing Noise Effects in Structural Health Monitoring Using Hilbert Transform of the Condensed FRF
A novel model updating-based damage detection method is proposed that uses the Unwrapped Instantaneous Hilbert Phase (UIHP) of the condensed frequency response function (CFRF) as input to the objective function of an optimisation problem. The novelty of the proposed method lies in two items: (1) using the CFRF instead of the FRF itself, and (2) using the UIHP associated with the columns of the CFRF as input. The proposed modifications bring about the following improvements in the damage detection practice as follows: (1) CFRF will reduce the number of required degrees of freedom (DOFs) to be measured, and (2) the UIHP mitigates the effect of the measurement noise on damage detection. The problem of damage detection in a laminated composite plate with different number of layers and ply orientation has been solved where the results demonstrate the effectiveness of the proposed method.
Investigating the Effects of Ambient Temperature on Feature Consistency in Vibration-Based SHM
Structural health monitoring (SHM) systems have been implemented across multiple engineering applications, and SHM remains an active area of research addressing the improved safety, reliability, and management of these structures. Several challenges, however, have limited the practical implementation and generalisation of SHM technologies, such as operational and environmental fluctuations, repeatability issues, and changes in boundary conditions. These inconsistencies can be problematic for SHM based on machine learning, as healthy states may be incorrectly flagged as damaged, or damaged states may be misclassified as normal variations. Likewise, manufacturing differences can result in variation among similar structures. Accounting for these variations is especially important for a population-based approach to SHM (PBSHM), which seeks to transfer valuable information, including normal operating conditions and damage states, across similar structures. This work aims to quantify this variability, and evaluate the applicability of SHM when these deviations occur. In this paper, an experimental campaign is discussed, in which vibration data were collected over a series of tests on a set of full-scale, composite glider wings. Tests were performed at multiple ambient temperatures, and with real and simulated damage conditions. The frequency response functions of the wings are examined to identify changes in natural frequency.
New Excitation (Multiple Width Pulse Excitation (MWPE)) Method for SHM Systems Part 2: Classification of Time- Frequency Domain Characteristics with 2DSSD and CNN
Surface response to excitation (SuRE) and electromechanical impedance methods quantify the difference between the reference and any given spectrums by calculating the sum of the squares of differences (SSD). In part one of this study, twodimensional SSD (2D-SSD) was proposed to quantify the difference of timefrequency plots when the part was excited with the Multiple Width Pulse Excitation (MWPE) signal. In this study, neural networks and deep learning were used for the classification of structural health monitoring (SHM) signals. Since manual encoding of the 2D spectrograms is very complicated to prepare them for classification by using neural networks, deep learning has been used. In this study, the performance of deep learning was evaluated for the classification of sensory data. A cross-shaped part made of PLA was manufactured additively and the center of the part was excited with MWPE and the surface waves were monitored at the end of each extension. Tests were repeated without and with a compressive force at each extension. The recorded time-domain sensory data was converted to spectrogram images using Short-Time Fourier Transform (STFT). The spectrograms were classified with the Convolutional Neural Network (CNN) after proper training. The results showed that the hidden geometry of each extension had a distinctive effect on the characteristics of the monitored signals. CNN could classify the infill type, skin thickness, and loading conditions with better than 92 % accuracy when the responses of the 20 pulses in the MWPE signal were considered.
Investigating Experimental Repeatability and Feature Consistency in Vibration-Based SHM
Structural health monitoring (SHM) has been an active research area for the last three decades, and has accumulated a number of critical advances over that period, as can be seen in the literature. However, SHM is still facing challenges because of the paucity of damage-state data, operational and environmental fluctuations, repeatability issues, and changes in boundary conditions. These issues present as inconsistencies in the captured features and can have a huge impact on the practical implementation, but more critically on the generalisation of the technology. Population-based SHM has been designed to address some of these concerns by modelling and transferring missing information using data collected from groups of similar structures. In this work, an experimental campaign is discussed, in which vibration data were collected over a series of tests on a set of four healthy, full-scale composite helicopter blades. During the tests, variability was introduced by adjusting boundary conditions between each testing repetition. It is well known that changes of boundary conditions, even from careful repositioning of the structure, can alter selected features properties, changing dynamic responses from normal condition and thus raising false alarms which degrade the effectiveness of SHM. In addition, nominally-identical structures may have slight differences in geometry and/or material properties. These variations can present as changes in the dynamic characteristics of the structure, which can be very problematic for SHM based on machine learning. This paper demonstrates the applicability of SHM when such deviations occur. In this work, a normal condition for the set of helicopter blades is established and tested via a point-wise outlier analysis approach and by defining a general model for the blades, called a population form, using Gaussian process regression.
Research on Structural Health Diagnosis Technology of Coke Drum Based on Monitoring Data
The coke drum, work under high temperature, coke in and out with hot and cold distress through the cycle of 500℃ to 50℃, is the core reactor of the delayed coking device. Typical failure modes of bulge deformation of tower, low cycle fatigue crack of weld, material aging, the tower body bending and inclining is easy to be caused under the comprehensive impact of complex load condition, temperature stress and mechanical stress of coke drums, which is a serious threat to safety production. The development and application of coke drum structural health monitoring system ensures the healthy operation of the equipment, and will effectively cope with the increasing long-term operation requirements of refining and chemical enterprises. Based on the stress monitoring data of the key parts of the coke drum, as well as the operating pressure and temperature data, this paper analyzes the characteristics of coke drum structure monitoring data and the causes of circumferential weld cracking of coke drum structure. The residual life of the monitored structure is evaluated by using the improved rain flow counting statistical method and miner fatigue cumulative damage theory. Finally, the contribution rate of different process sections of coke drum to its structural damage is analyzed. This study provides a reference for structural health monitoring and operation process optimization of coke drum.
Experimental Investigation on a Novel Osseointegrated Implant Stability Assessment Using on Vibration Analysis
Osseointegrated prostheses are widely used as the treatment for femur amputation. However, this technique requires sufficient implant stability before and during the rehabilitation period to mitigate the risk of implant breakage and loosening. Hence, reliable assessment methods for the osseointegration process are essential to ensure initial and long-term implant stability. This paper aims to investigate a vibration analysis method with a novel implant design, which focuses on the analysis of the dynamic response of the femur-implant system during the simulated osseointegration process. The paper also proposes a concept of using normalized energy difference to formulate an energy index (E-index). A 133mm-long amputated artificial femur model was constrained at the proximal end with a customized clamp. The epoxy adhesives were applied at the interface between the aforementioned femur and implant to simulate the change in stiffness in mimicking the osseointegration process. A two-unidirectionalsensor setup attached to the bottom of the implant was used to record the dynamic response stimulated by an impact hammer. The results show a significant change in magnitude of the cross-spectrum during the osseointegration processes. The resonance modes in cross-spectrum for the frequency above 1000Hz are hard to distinguish suggested that the vibration of the system being hindered by the high dampening effect of the adhesive before the initial bonding of the adhesive at 300s. The plot of E-index shows a clear correlation that the E-index provided a potential quantitative approach for monitoring the stages of osseointegration. These findings highlight the feasibility of using the vibration analysis technique and E-index to quantitatively monitor the osseointegration process for future improvement on the efficiency of human health monitoring and patient rehabilitation.
Detecting Bending and Impact Events in a Fiber Optic Cable Using Distributed Acoustic Sensing to Assess Potential Offshore Power Cable Damges
A key vulnerability of offshore energy production facilities are submarine power cables. Monitoring a power cable during its entire lifetime is a strategy to minimize critical damages. Submarine power cables contain fiber optic cables that can be used for monitoring purposes. In this paper we show that a recent fiber optic sensing technique (CP-ΦOTDR) [11] is capable of detecting bending and impact events in a fiber optic cable. It does so with a limited reproducibility however, which for impact events cannot be solely explained by the variability of the impact force. There is a slight signature present for impact events: identical impact force events correlate more than events belonging to non-identical forces. This work is a first step in developing a monitoring tool to help assess the severity of power cable damages due to incident impacts and critical bending states.
On Robustness of Optimal Sensor Placement to Environmental Variation for SHM
One challenge in establishing an effective structural health monitoring (SHM) system is the impact of environmental variability on damage identification. It is therefore, advantageous to consider any environmental effects when conducting sensor placement optimisation (SPO). One approach to this problem is to check the robustness of SPO technique to environmental variations and consider whether it is necessary to take account of these environmental factors as part of the optimisation process. This paper will study the robustness of an SPO method to variations in the ambient temperature of the structure. Two kinds of data, including the mode shapes and the Mahalanobis squared-distance (MSD), from tests on a glider wing structure are used as features for SPO separately. This structure was set up and tested in different health states across a series of controlled temperatures. The results show that the SPO results obtained via the mode shapes are robust to the temperature variation, while the SPO results corresponding to MSD are sensitive to temperature changes.
Monitoring of Thermal Ageing Cycles of a Silicone Adhesive in a Simulated Space Environment Using Embedded TFBG Sensors
This research demonstrates the promising abilities of a tilted Fibre Bragg Grating (TFBG) sensor for monitoring the status of a silicone adhesive during a simulated space environment exposure. The silicone is used as adhesive between two thin cover glasses and the TFBG is embedded into the polymer such that it is fully enclosed. Then, the sample is exposed to standard space environment conditions in a vacuum chamber simulated by creating a high vacuum (1.3×10-6 mbar) and thermal cycles between -120 ℃ to 190 ℃. The TFBG spectra recorded during the exposure were demodulated to obtain the wavelength shifts of the Bragg and Ghost peaks and the envelope area of the upper and lower cladding modes resonances peaks. This will allow the thermomechanical and the refractive index (RI) variations of the silicone to be measured during the testing. In particular, the silicone RI depends on the material chemical and physical state and its thermal history, and the TFBG envelope area is sensitive to these RI changes. Hence, the envelope area of the TFBG spectrum can be used to obtain information on the evolution of the silicone adhesive during the test. The resulting trend of the selected peak wavelengths variation and envelope area were used to detect a variation of the degradation state of the material.
Physics-Informed Neural Network Approach for Identification of Dynamic Systems
In this study, a novel method for online and real-time identification of dynamic systems is presented. This method is based on the newly introduced algorithm Physics Informed Neural Network (PINN). In order to find the dynamic characteristics of the system, sparse displacement measurements are fed to the Artificial Neural Network (ANN); By introducing the classic vibration equation of the system to the ANN as a physics constraint, the PINN estimates both dynamic characteristic and state of the system. The proposed framework is evaluated by several numerical studies with different system properties, noise levels, architecture, and training data. On that account, four structural systems are presented: (1) single-degree-of-freedom (SDOF) systems with different properties and noise levels, as basis model with an accurate analytical solution (2) a three-degree-of-freedom (3-DOF) system with both complete and sparse measurements, representing the structural model of the n-story shear frames (3) a simple supported beam subjected to an initial displacement with several NNs architecture and sensor numbers, and (4) a Pure Cubic Oscillator (PCO) as a nonlinear dynamic system. The results of the proposed platform for the PINN are compared to a mutual ANN in all cases to emphasize the superiority of the PINN in both determining the dynamic characteristics and state estimation of dynamic systems. In addition, the performance of both NNs is examined with different training data to ensure the resilience of the algorithm and affirm the role of the added criteria, physics constraint, in reducing the dependency on the training data. The proposed algorithm can accurately estimate the dynamic characteristics of different dynamic systems with sparse, noisy measurements; by means of the classic dynamic equations and smartly selection of the hidden layer numbers, the PINN will be a powerful predictive tool for the dynamic analysis in the absence of any prior knowledge of the dynamic systems.
Study of the Effects of Thermal Stress on Piezoelectric Sensors for the Structural Health Monitoring
During the in-service lifetime of an aircraft, the surface bond of any SHM transductor is submitted to thermal stresses, induced by aviation environmental conditions. In this paper, the influence of a disbond, whether willingly introduced or caused by thermal aging, on the ability of a PZT ultrasonic transducer to generate and receive Lamb waves is numerically and experimentally studied.
A New Method in Conceptual Design of the SHM Airframe Using Rough Sets
A very promising solution in airframe design is a new trend in application based on intelligent online condition monitoring known as Structural Condition/Health Monitoring/Management System, which has evolved towards Integrated System Health Management to cover the whole System-of-Systems architecture. This paper focuses on how to reduce Big Data with reference to a comprehensive model for designing future airframe heath monitoring system, including the problem of assessing the diversity of various sensors used in the assessment of the technical condition/health of an airframe, which generates big data sets with different records of measurement data that must be reduced for effective using. The paper summarizes the research on the assessment of sensors in terms of their suitability and effectiveness in monitoring the airframe condition, including metal and composite, and refers to the complexity of the problem by integrating the technical condition/health of the engine unit and the airframe. Integration issue of the reliability serial connection of the airframe and engine for airworthiness assessment were mentioned. A new method based on the rough set paradigm, including hybrid variants, for the reduction of big data of health monitoring into their effective management were postulated and advantages were demonstrated. The proposed method in the form of listed issues is complementary to the currently used method and is aimed at supporting the design of the airframe at the preliminary assessment stage. That method is strongly recommended to increase security and reduce computational cost and operational cost including maintenance cost in aircraft life-cycle costs.
Assessing the Information Content of Datasets for Structural Health Monitoring
Data-based SHM is highly dependent on the quality of the training data needed for machine learning algorithms. In many cases of engineering interest, data can be scarce, and this is a problem. However, in some cases, data are abundant and can create a computational burden. In data-rich situations, it is often desirable to select the subset(s) of the data which are of highest value (in some sense) for the problem of interest. In this paper, value is interpreted in terms of information content, and entropy is used a measure of that content in order to condense training data without compromising useful information. Using the minimum covariance determinant, the dataset is first separated using inclusive outlier analysis. The entropies of the separated datasets are then assessed using parametric and nonparametric density estimators to identify the subset of data carrying most information. The Z24-Bridge dataset is used here to illustrate the idea, where the entropy values indicate that the subset containing data from environmental variations and damage is most rich in information. This subset was made up of half of the entire dataset, suggesting that it is possible to significantly reduce the amount of training data for an SHM algorithm whilst retaining the required information for analysis.
Quantifying the Value of Vibration-Based Structural Health Monitoring Considering Environmental Variability
The value of structural health monitoring (SHM) can be quantified as the difference in expected total life-cycle costs between two different maintenance planning strategies, one representing the standard means to assessment, namely intermittent visual inspections, and the other based on availability of continuous SHM data. We show how to quantify the value of vibration-based SHM conditional on a damage history over the structural lifetime. We showcase the analysis through application on a numerical benchmark model of a two-span bridge system subjected to gradual deterioration and sudden damages in the middle elastic support over its life-cycle, simulating the case of scour. The effect of environmental variability is included in the analysis by means of a stochastic model for the dependence of the Young's modulus on temperature (E-T). The numerical investigations provide insights related to the effect of the temperature variability, as well as the visual inspections quality, on the value of SHM.
Accuracy Improvement of a Degradation Model for Failure Prognosis of Miter Gates
Aims to address the issue that the degradation model may not accurately represent the underly true degradation physics in failure prognostics of miter gates, this paper presents a framework for degradation model correction using historical strain measurements. A stochastic gap growth model with uncertain model parameters is employed as the simplified degradation model to predict the gap evolution. A dynamic model discrepancy quantification framework is then proposed to correct the simplified model by representing the model bias term as a data-driven surrogate model. After that, a maximum likelihood estimation method is developed to estimate the parameters of the data-driven surrogate model using strain measurements. Additionally, the uncertainty in the model parameters of the simplified model is reduced using Bayesian method. The corrected and updated simplified degradation model is then employed for failure prognostics of a miter gate. Results of a case study show that the updated degradation model can accurately predict multi-step ahead gap growth while performing damage prognostics and remaining useful life estimation.
Warped Gaussian Processes for Prognostic Health Monitoring
Gaussian process regression is a powerful method for predicting states associated with uncertainty. A common application field is to predict damage states of structural systems. Recently, Gaussian processes became very popular as they deliver credible intervals for the predicted states. However, one major disadvantage of Gaussian processes is that they assume a normal distribution. This is not justified when the relevant variables can only assume positive values, such as crack lengths or damage states. This paper presents a way to bypass this problem by using warped Gaussian processes: We (1) transform the data with a warping function, (2) apply Gaussian process regression in the latent space, and (3) transform the results back by using the inverse of the warping function. The method is applied to a crack growth example. The paper shows how to integrate prior knowledge into warped Gaussian processes in order to increase prediction accuracy and that warped Gaussian processes lead to better and more plausible results.
Nonlinear Bulk Wave Propagation in a Material with Randomly Distributed Symmetric and Asymmetric Hysteretic Nonlinearity
The nonlinear ultrasonic technique is an effective nondestructive testing technique for the detection of very small early-stage damages in solids that often remain insensitive to linear ultrasonic techniques. Interaction of a single frequency (f) ultrasonic wave with early-stage complex non-linear micro-defects such as dislocations, grains, micro-cracks, and micro-pores, etc., generates ultrasonic waves with higher harmonics (2f, 3f, 4f, 5f). In theoretical and computational studies, early-stage damages are modeled as homogeneous nonlinear material models like quadratic, cubic, and hysteretic nonlinearities. Interaction of single frequency wave with quadratic nonlinearity generates both odd and even harmonics but in the case of cubic and symmetric hysteretic nonlinearity, only odd harmonics generated due to material nonlinearity are observed. Symmetric hysteretic nonlinearity shows symmetric hysteretic force versus displacement hysteretic curves. In practice, the early-stage damages are highly localized and randomly distributed, and hysteretic. The objective of this investigation here is to study the interaction of a single frequency and two-frequency (one-way two-wave mixing) ultrasonic waves with the randomly distributed hysteretic nonlinear local damages. As the theoretical studies in hysteretic nonlinearities are challenging, here a numerical study of such a complex nature of wave propagation is considered. Symmetric and asymmetric hysteretic nonlinearities are considered independently. A one-dimensional spatial domain discretized as a long-chain of springmass elements with a random distribution of hysteretic spring elements. For symmetric hysteretic element, famously used Bouc-Wen model implemented and for asymmetric hysteretic element recently proposed Generalized Bouc-Wen model is implemented to capture asymmetric hysteretic force versus displacement nature of hysteretic curves. A single-frequency Gaussian pulse is sent from the left end of the spatial domain and the time responses are recorded at the one-fifth of total length and right end of the spatial domain. The same computational experiments repeated for ten different cases of randomly distributed symmetric and asymmetric hysteretic elements each and independently. In both the symmetric and asymmetric cases, harmonically scattered waves from randomly distributed local nonlinearities with sufficiently less amplitude than the input wave are observed. The frequency response of the recorded waves shows only odd harmonics in the case of randomly distributed symmetric hysteretic nonlinear damages, but in the case of asymmetric hysteretic nonlinear damages, both the odd and harmonics are observed. The evolving symmetric and asymmetric hysteretic curves are observed in the case of symmetric and asymmetric hysteretic nonlinear damages respectively. To understand the one-way two-wave mixing phenomenon in thesehysteretic nonlinearities, a Gaussian pulse with two input frequencies is sent from the left end of the spatial domain. Due to mixing in symmetric hysteretic damage, sum and difference frequencies corresponding to only odd harmonics of input frequency along with the odd original odd harmonics are seen. In asymmetric hysteretic damage cases, sum and difference frequencies corresponding to both the odd and even harmonics are observed along with the original odd and even harmonics. In mixing significant amount of input energy is supplied to the possible frequency combinations present near the first few odd and/or even harmonics.
Microcracks Detection of Thermal Damaged Concrete with Nonlinear Ultrasonic Modulation Based on Broad Band Frequency Coupling
The initiation and development of microcracks introduced by heating or fire plays a critical role in the stability and durability of concrete structure. However, the concealment of microcracks and the nonlinearity of concrete materials make it difficult to appropriately evaluate the size and extension state of microcracks inside the thermal damaged concrete. In this paper, broadband frequency excitation instead of traditional dual-frequency excitation is utilized to excite the thermal damaged concrete, and the generated ultrasonic modulated signal reflects the micro damage state. The concept of damage index (DI) based on the sideband peak count (SPC) is proposed to quantitatively describe the variation characteristics of modulated signals. The results show that the peak value of DI based on broadband frequency coupling of nonlinear ultrasonic modulation method reflects the generation and development of microcracks in thermal damaged concrete. The peak value of DI increases sensitively with the increasing of water-cement ratio, fine-coarse aggregate ratio, and the heating temperature. Meanwhile, the statistical relationships of the peak value of DI with the residual strength and the area ratio of microcracks in thermal damaged concrete are established respectively.
Robust Detection of Damage in Composite Plates Using the Nonlinear SPC-I Ultrasonic Technique
The newly developed non-linear ultrasonic (NLU) technique known as the Sideband Peak Count - Index (SPC-I) has demonstrated that it can detect and monitor the non-linearity generated by defects in a wide range of materials such as metals, composites, and concrete. The general approach of applying the SPC-I technique is by using a single sweep wideband excitation signal that is propagated through the specimen and a single signal is received which is then analyzed. This general approach has proven to be effective in giving a big picture measure of the nonlinearity of the material. However, it can be further tuned and improved by exciting a sweep signal using multiple excitation signals. As a result, multiple signals are received and analyzed. These multiple sweep signals have the benefit of not being contaminated (dispersion effects) by multiple wave modes propagating at the same time compared to exciting a wide band single sweep signal. Additionally, by using these multiple sweep signals the effects of frequency modulation of wave modes and higher harmonics are easier to detect. By analyzing the received signals multiple frequency ranges can be discovered that are sensitive to different failure modes or types of defects. These frequency ranges of interest are then used to detect damage initiation and progression in the composite plate specimens. Two sets of composite plate specimens with two types of fiber reinforcements (Glass and Basalt) are investigated in this study. The specimens are impacted with a dart impact machine at increasing impact energies. By focusing on a frequency range that is sensitive to the damage in the composite plate specimens. The NLU SPC-I technique can robustly detect and monitor the impact induced damages in composite plates.
Harmonic Scattering of SH Waves from a Localized Damage: Finite Element Studies
Interaction of the monochromatic ultrasonic wave with the uniformly distributed micro-scale damages modeled as a weakly nonlinear solid generates higher harmonics. In practice, most of the metallic components under fatigue and fracture introduce highly localized early-stage damages like local plasticity and region of micro-cracks resulting in shear bands, etc. Theoretical studies by Tang (2012), Wang and Achenbach (2017), Kube (2018), and Wang et.al. (2019) on the interaction of the ultrasonic waves with localized material nonlinearities discuss the interesting effects of the scattered harmonic waves. Low amplitudes of the backscattered harmonic waves and the requirement of a specific set of wideband transducers make experimental studies hard. On the other hand, finite element studies presented in this paper demonstrate the interesting nature of harmonic scattering of the SH waves which will be helpful for the effective design of the laboratory experiments. Generation of backscattered Lamb waves by the interaction of SH wave with local damage due to monochromatic wave is verified with the analytical solution presented by Wang et.al. (2019). Only odd harmonics of the forward scattered SH waves are noted and only even harmonics of both backscattered and forward scattered Lamb waves are noted. Higher amplitudes of static components of Lamb waves are observed due to their cumulative nature similar to higher harmonics. The effect of harmonic scattering in nonlinear guided wave mixing is also studied by considering one-way and two-way two-wave mixing of SH waves. A greater number of sum and difference frequencies along with the odd and even harmonics of both the backscattered and forward scattered waves are noted in codirectional wave mixing, as the complete local damage region is covered by the mixing zone. In two-way mixing, the zero group velocity Lamb waves are generated at the local nonlinear material region. Both backscattered and forward scattered waves contain only Lamb waves with sum and difference frequencies and corresponding odd and even harmonics. To understand the effect of the intensity and size of the localized nonlinear material region various studies are carried out by scaling nonlinear material parameters and geometric size of the local damages (0-20 mm). Observed various characteristics of harmonically scattered waves show their potential in quantifying the intensity, size, and position of local damages by solving simple inverse problems.
Impact Detection and Location in Operational Environment: An Acoustic Emission Approach
Acoustic emission is a non-destructive inspection technique that has been used in a wide variety of applications. However, it has also shown the potential to be used as a structural health monitoring system when the sensors are permanently attached to the structure. For this purpose, PZT wafers (PWAS) have proved to be more suitable as they are less bulky and costly than the standard commercial transducers. Furthermore, some studies have recently shown the benefits of PWAS over encapsulated acoustic sensors, which mainly rely on the capability to measure in-plane (symmetric) and outof- plane (antisymmetric) guided wave motions. The possibility to capture all the elastic wave information opens the potential to identify, locate and characterize on-line the events which are taking place on the structure, e.g. impacts, delaminations, fatigue crack, etc. The normalization of the acquired signals under varying operational and environmental conditions is a common challenge shared among all the monitoring techniques and is strongly dependent upon the application. Some authors have shown the influence of temperature and simulated vibration in impact damage localization, however experimental demonstration of impact location in relevant operating conditions was lacking. In this study, the detection and location of a low energy impact event in thick aluminium plates using sparse sensor clusters have been implemented and demonstrated under a cruise vibration profile of a jet aircraft. As the time of flight (ToF) is unaffected by the external vibrations, a location algorithm based on ToF has been implemented, and its probability of detection and prediction error evaluated. It has been concluded that an acoustic emission system using PWAS could be a feasible solution for the passive monitoring of an in-flight aerostructure.
Acoustic Emission Signal Processing Study of Nanoindentation on Thin Film Stack Structures Using Gaussian Mixture Model
This investigation utilizes a material testing system that integrates acoustic emission (AE) testing with a nanoindentation system for crack generation and detection in Al-Cu top thin-film stack structures. The suitability of using the AE method was verified with scanning electron microscope (SEM) images of indent cross-sections. In order to cluster the AE signals based on a different physical meaning, a signal processing approach based on the Gaussian mixture model (GMM) clustering algorithm was applied. Principal component analysis (PCA) and autoencoder feature extraction methods were used to reduce the dimension of the signal. This signal processing approach has the promising ability to distinguish AE events associated with crack formation and metal layer plastic deformation. This integrated test system and signal processing approach provide a high-resolution mechanical testing platform for studying and enabling automatic, non-destructive crack detection in wafer probing.
Detection and Evaluation of Impact Damage on Aircraft Control Surface Using Acoustic Emission and Convolution Neural Network
Impact damage is one of the major threats to the integrity of aircraft control surfaces such as wings and elevators. The conventional and widely applied inspection approach is visual inspection which is time-consuming and subject to human error. The innovation of this paper lies in developing a smart sensing system by leveraging acoustic emission (AE) for the real-time detection and evaluation of impact damage on aircraft elevators. The challenge of this system is to deploy a minimal number of AE sensors on the aircraft due to the environmental restriction during the operation of the aircraft while still effectively evaluate the impact damage. A convolutional neural network (CNN) is employed to localize the impact and evaluate the damage level by analyzing the wavelet of signals obtained by a single AE sensor. The proposed approach is verified by an impact test carried out on a thermoplastic aircraft elevator. The results demonstrate the efficacy and potential of the proposed approach.