Application of Orthogonal Decomposition Approaches to Long-Term Monitoring of Infrastructure Systems

    Authors

    E. Kallinikidou; H. B. Yun; S. F. Masri; J. P. Caffrey;L. H. Sheng

    Comments

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    Abbreviated Journal Title

    J. Eng. Mech.-ASCE

    Keywords

    Structural health monitoring bridges; Uncertainty quantification; Nonparametric identification; Orthogonal decomposition; SUSPENSION BRIDGE; IDENTIFICATION; Engineering, Mechanical

    Abstract

    The long-range monitoring of civil infrastructure systems monitored with dense sensor arrays that are capable of generating voluminous amounts of data from continuous online monitoring requires the implementation of a proper data processing and archiving scheme to maximize the benefits of structural health monitoring operations. This paper focuses on the areas of data management, data quality control, and feature extraction of meaningful parameters to describe the response of large-scale infrastructure systems to ambient excitation in the context of structural health monitoring (SHM). Recordings from the monitoring system installed on the Vincent Thomas Bridge (VTB) in San Pedro, California form the database of the proposed data-management and archiving methodology. The data processing methodology for the VTB is based on the calculation of the sensor array acceleration covariance matrices for every hour of available data and the subsequent orthogonal decomposition of the covariance matrices. The dominant proper orthogonal modes of the bridge are determined, and their statistical variations over an extended observation period covering several months of continuous data are quantified and analyzed. The empirical probability density functions for the mean daily bridge accelerations are computed and used to compare the statistical variations in different periods of operation of the bridge (working days, weekends, holidays). It is shown that the computed statistical distributions of the bridge response can provide a quantitative baseline through which to facilitate the early detection of any anomalies indicative of a possible structural deterioration resulting from fatigue (service loads) or extreme loading events, i.e., earthquakes, artificial hazards, or other natural hazards. DOI: 10.1061/(ASCE)EM.1943-7889.0000331.(C) 2013 American Society of Civil Engineers.

    Journal Title

    Journal of Engineering Mechanics-Asce

    Volume

    139

    Issue/Number

    6

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    678

    Last Page

    690

    WOS Identifier

    WOS:000318569600002

    ISSN

    0733-9399

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