Nonparametric analysis of structural health monitoring data for identification and localization of changes: Concept, lab, and real-life studies
Abbreviated Journal Title
Struct. Health Monit.
Structural health monitoring; bridge; damage detection; damage; localization; strain data; correlation; laboratory; field testing; movable bridge; STATISTICAL PATTERN-RECOGNITION; DAMAGE DETECTION; FAULT-DETECTION; BRIDGE; ISSUES; Engineering, Multidisciplinary; Instruments & Instrumentation
Structural health monitoring systems integrate novel experimental technologies, analytical methods, and information technologies for a number of objectives such as detecting structural changes and damage as well as assessing the condition, safety, and serviceability of the monitored structure. The objective of this article is to present a correlation-based methodology as an effective nonparametric data analysis approach for detecting and localizing structural changes using strain data under operational loading conditions. While several methods have been explored in the literature, the focus of this article is to explore a practical and cost-effective (in terms of sensor, data acquisition, and analysis) methodology to identify structural problems. The methodology presented here is based on tracking correlation coefficients between strain time histories at different locations. After discussing the background, the effectiveness of the methodology is first demonstrated on a laboratory test structure. A unique contribution of this study is the validation of the methodology on a real-life bridge, which was monitored before damage was induced, during the bridge was damaged, and after damage was repaired. It is shown that structural changes can be detected and located for both the laboratory test structure and the real-life bridge using the variations in the correlation matrices. Since the real-life bridge was monitored under different conditions, the effectiveness of the bridge repair is also presented in comparative fashion with respect to before damage conditions. Some of the critical issues such as signal processing, data length, and level of data separation for change detection are also discussed. The correlation-based data analysis methodology is computationally efficient and easy to use, especially for handling large amounts of monitoring data. The results show that this methodology has the potential to be easily applied by engineers to different kinds of civil infrastructure for condition monitoring and maintenance.
Structural Health Monitoring-an International Journal
"Nonparametric analysis of structural health monitoring data for identification and localization of changes: Concept, lab, and real-life studies" (2012). Faculty Bibliography 2010s. 2363.