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

Autor: E. Kallinikidou, L.-H. Sheng, Sami F. Masri, Hae-Bum Yun, J. P. Caffrey
Rok vydání: 2013
Předmět:
Zdroj: Journal of Engineering Mechanics. 139:678-690
ISSN: 1943-7889
0733-9399
DOI: 10.1061/(asce)em.1943-7889.0000331
Popis: 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.
Databáze: OpenAIRE
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