First Steps Toward Integration of Structural Health Monitoring and Non-Destructive Evaluation Data for Decision Making

Abstract

In order to bring objective and mechanistic approaches to the repair and retrofit of highway bridges, multi-source data should be collected and properly integrated to provide effective bridge condition assessment to allow better asset management decisions. Although information from multiple Structural Health Monitoring (SHM) and Non-Destructive Evaluation (NDE) approaches may lead to more data and possibly to more reliable condition assessment, it may often not prove economical to deploy several monitoring or non-destructive methodologies. Visual inspection is usually the first and often the only step to periodically obtain information on bridge conditions (such as visible corrosion, cracks and vibration) and possibly observe the causes leading to deterioration of highway structures (such as trapped water, road salt, temperature, live loads, wind and others). Meanwhile visual inspection has some known limitations; for instance, it cannot commonly recognize unknown reserve capacities nor discover deficiencies that are not visible (e.g. delaminated deck) leaving much uncertainty in the actual condition and performance of existing bridges. It has been advocated for many years that in conjunction with visual inspection, objective in-depth assessment technologies such as monitoring operational responses and NDE should be deployed. However, when multiple sensing approaches are used, an integration of the collected data becomes necessary for proper interpretation and decision making. Effective integration of information collected from different SHM and NDE sources can be very challenging. This study focuses on optimal and practical ways to obtain and integrate measurement data with other proxy information (such as past inspection and maintenance records, traffic information, National Bridge Inventory (NBI) data, climate and weather data and others). Such integration strategies, if proven effective, could lead to more reliable interpretations of bridge condition and performance, and benefit highway asset management.