On the Impact of Prior Engineering Perception on Structural Health Diagnosis: Analysis of a Case Study

Abstract

We illustrate an application of Bayesian logic to analysis of monitoring data and to inference of structural condition. The case study is a 260 m cable-stayed bridge spanning the Adige River 10 km north of the town of Trento, Italy. This is a statically indeterminate structure, having a composite steel-concrete deck, supported by 12 stay cables. Structural redundancy, possible relaxation losses and an as-built condition differing from design, suggest that long-term load redistribution between cables can be expected. To monitor load redistribution, the owner installed a monitoring system that includes fiber-optic sensors. These sensors are FBG-based and allow measurement of changes in deformation with an accuracy of the order of just a few microstrains, with respect to the value at installation. After one year of system operation, which included maintenance on the interrogation unit, the data analysis showed an apparent contraction of the cable lengths. This result is in contrast with the expected behavior, which should be of elongation, due to creep, shrinkage and relaxation. We analyze how a rational agent makes sense of the observed response, and particularly we discuss to what extent he/she is prone to accept the sensor response as the result of the real mechanical behavior of the bridge versus a mere malfunction of the interrogation unit. In this exercise we consider four psychological profiles, which vary based on their personal trust in the reliability of the instrumentation and on their knowledge of the structural behavior of the bridge. Using Bayesian logic as a tool to combine prior belief with sensor data, we highlight how the extent of prior knowledge can alter the final engineering perception of the current state of the bridge.