Structural damage detection of old ADA steel truss bridge using vibration data

Abstract

This study proposes a statistical-based detection method that is responsive to damage and not to environmental and operational conditions. The method serves as a damage recognition system for structural health monitoring using field measurements from real bridges. Vehicle-induced bridge and ambient vibration measurements collected from the benchmark Old ADA steel truss bridge situated in Japan were utilized to validate the proposed method. The steel truss members in the bridge were subjected to five different damage scenarios to represent common potential problems in structural health monitoring of real-life applications. The collected measurements have been completely published and made available online. A combination of principal component analysis (PCA) and linear discriminant analysis (LDA) transformation is utilized as a statistical-based recognition technique. Vibration data representing power spectral density (PSD) functions were tested as damage-sensitive features from identified condition sources. The proposed combination of the PCA-LDA transformation system outperforms the popular PCA transformation as a statistical model for classification of state conditions. Although the first two principal components of PCA hold 50-85% of the variation in data, the first two components from PCA-LDA hold about 95% of the total variation. As a result, the three PCs, of PCA-LDA, visualization successfully managed to classify the five structural damage scenarios into their five individual subgroups.