Structural Damage Detection in a Truss Bridge Model Using Fuzzy Clustering and Measured FRF Data Reduced by Principal Component Projection

Abstract

This study deals with vibration-based damage detection in a truss bridge model and suggests a novel methodology based on fuzzy clustering and measured frequency response function (FRF) data reduced by principal component projection. A six-bay truss bridge model is designed and fabricated in laboratory, various connection damages are simulated by loosening the end connecter bolts, and the environmental effects are taken into account by changing in excitation force levels of a shaker. The FRFs of the healthy and the damaged structure are used as initial data. The FRF data normalization is performed for eliminating the effects caused by the environmental and operational variability. Two data projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA) are adopted for data compression and the median values of principal components are defined for damage feature extraction. The fuzzy c-means (FCM) clustering algorithm is used to categorize these features for structural damage detection. The illustrated results show that the proposed method can effectively identify the bridge damages simulated by loosening the bolted joints of the truss bridge structure. It is sensitive to the structural damage but it is non-sensitive to the effect of the environmental and operational variations. This makes it quite generic and permits its potential development for real and complex truss bridges in site.

Keywords

structural health monitoring truss bridge structural damage detection eigenspace projections fuzzy clustering

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