An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis

    Authors

    M. Malekzadeh; M. Gul; I. B. Kwon;N. Catbas

    Comments

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    Abbreviated Journal Title

    Smart. Struct. Syst.

    Keywords

    structural health monitoring; fiber optic sensor; non-parametric damage; detection algorithm; principal component analysis; cross correlation; analysis; FAULT-DETECTION; IDENTIFICATION; BRIDGE; Engineering, Civil; Engineering, Mechanical; Instruments &; Instrumentation

    Abstract

    Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

    Journal Title

    Smart Structures and Systems

    Volume

    14

    Issue/Number

    5

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    917

    Last Page

    942

    WOS Identifier

    WOS:000347932200010

    ISSN

    1738-1584

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