Damage detection using data-driven methods applied to moving-load responses

Developed economies depend on complex and extensive systems of infrastructure to maintain economic prosperity and quality of life. In recent years, the implementation of Structural health monitoring (SHM) systems on full-scale bridges has increased. The goal of these systems is to inform owners of the condition of structures, thereby supporting surveillance, maintenance and other management tasks. Data-driven methods, that involve tracking changes in signals only, are well-suited for analyzing measurements during continuous monitoring of structures. Also, information provided by the response of structures under moving loads is useful for condition assessment. This paper discusses the application of data-driven methods on moving-load responses in order to detect the occurrence and the location of damage. First, an approach for using moving-load responses as time series data is proposed. The work focuses on two data-driven methods - Moving principal component analysis (MPCA) and Robust regression analysis (RRA) - that have already been successful for damage detection during continuous monitoring. The performance of each method is assessed using data obtained by simulating the crossing of a point-load on a simple frame. (C) 2013 Elsevier Ltd. All rights reserved.