Application of Multivariate Statistically Based Algorithms for Civil Structures Anomaly Detection
Two multivariate statistics based damage detection algorithms are explored in conjunction with optical fiber sensors for long-term application of Structural Health Monitoring. Two newly developed data driven methods are investigated, for bridge health monitoring, here based on strain data captured by Fiber Bragg Grating (FBG) sensors from 4-span bridge model. The most common and critical damage scenarios were simulated on the representative bridge model equipped with FBG sensors. Acquired strain data were processed by both Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA). The efficiency of FBG sensors, MPCA and MCCA for detecting and localizing damage is explored. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to observe and detect both local and global damage implemented on the bridge structure.
KeywordsStructural health monitoring Fiber Bragg Grating Sensors Advanced multivariate statistics Damage detection
The authors would like to acknowledge Dr. Il-Bum Kwon from KRISS Korea for his expertise and support for the fiber optic sensing development and work at the University of Central Florida. For this, the authors are grateful to Dr. Kwon for his guidance and know-how. The research project described in this paper is supported by the Federal Highway Administration (FHWA) Cooperative Agreement Award DTFH61-07-H-00040. The authors would like to express their profound gratitude to Dr. Hamid Ghasemi of FHWA for his support of this research. The authors would also like to acknowledge the contributions of their research collaborators and their research team. The opinions, findings, and conclusions expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring organization.
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