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Clustering Studies for Damage Detection in Bridges: A Comparison Study



Improved and more continuous condition assessment of bridges has been demanded by our society to better face challenges presented by aging civil infrastructure. In the last two decades, bridge condition assessment techniques have been developed in order to improve the maintenance of bridges in a systematic way. The Structural Health Monitoring (SHM) has given the ability to provide information, in real time, about the performance of the structural system. However, the reliability of that information depends highly on the quality of the data analysis, as the operational and environmental variability introduces changes into the system that can mask those changes related with damage. Therefore, this paper intends to evaluate the performance of several algorithms for clustering strategies in vibration-based damage detection under operational and environmental effects. For statistical modeling and feature classification are proposed K-means, Gaussian Mixture Models (GMM), Support Vector Clustering (SVC) and Self-Organizing Maps (SOM) algorithms. The study is performed on standard data sets from the Tamar Suspension Bridge, in England, and the Z-24 Bridge in Switzerland. The contribution of this work is the applicability of the proposed algorithms for clustering strategies in damage detection as well as the comparison of the classification performance between these algorithms.

doi: 10.12783/SHM2015/146

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