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5 April 2017 Application of the normalized curvature ratio to an in-service structure
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Fiber optic sensors (FOS) offer numerous advantages for structural health monitoring. In addition to being durable, lightweight, and capable of multiplexing, they offer the ability to simultaneously monitor both static and dynamic strain. FOS also allow for the instrumentation of large areas of a structure with long-gages sensors which helps enable global monitoring of the structure. Drawing upon these benefits, the Normalized Curvature Ratio (NCR), a curvature based damage detection method, has been developed. This method utilizes a series of long-gage fiber Bragg grating (FBG) strain sensors for damage detection of a structure through dynamic strain measurements and curvature analysis. While dynamic SHM methods typically rely up frequency and acceleration based analysis, it has been found that strain and curvature based analysis may be a more reliable means for structural monitoring. Previous research was performed through small scale experimental testing and analytical models were developed and provided promising results for the NCR as a potential damage sensitive feature. Based on this success, this research focuses on the application of the NCR to an existing in-service structure, the US202/NJ23 highway overpass located in Wayne, NJ. The overpass is currently instrumented with a series of long-gage FBG strains sensors and periodic strain measurements for dynamic events induced by heavy weight vehicles have been recorded for more than 5 years. This research shows encouraging results and the potential for the NCR to be used as a simplistic metric for damage detection using FBG strain sensors.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kaitlyn Kliewer and Branko Glisic "Application of the normalized curvature ratio to an in-service structure", Proc. SPIE 10170, Health Monitoring of Structural and Biological Systems 2017, 101702K (5 April 2017);


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