Title

Application Of Multivariate Statistically Based Algorithms For Civil Structures Anomaly Detection

Keywords

Advanced multivariate statistics; Damage detection; Fiber Bragg Grating sensors; Structural health monitoring

Abstract

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. © The Society for Experimental Mechanics, Inc. 2013.

Publication Date

9-3-2013

Publication Title

Conference Proceedings of the Society for Experimental Mechanics Series

Volume

39

Issue

4

Number of Pages

289-298

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84883179960 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84883179960

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