Anomaly Detection of Bridges Based on Bayesian Inference of Multivariate Auto-Regressive Model

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  • GOI Yoshinao
    Department of Civil and Earth Resources Engineering, Kyoto University
  • KIM Chul-Woo
    Department of Civil and Earth Resources Engineering, Kyoto University

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  • 橋梁振動の多変量自己回帰モデルのベイズ推論に基づく異常検知
  • キョウリョウ シンドウ ノ タヘンリョウ ジコ カイキ モデル ノ ベイズ スイロン ニ モトズク イジョウ ケンチ

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Abstract

<p>This study proposes an anomaly detection method for bridges using Bayesian inference, aiming at efficient inspection based on vibration monitoring. In the proposed method, firstly a posterior distribution of the parameters composing multivariate auto-regressive model is acquired from a bridge under healthy condition by means of Bayesian inference. Secondly, based on the posterior distribution representing vibration of the healthy bridge, a Bayes factor is calculated to detect change in the modal properties caused by damage. To investigate feasibility of the proposed method for damage detection, this study utilized data from a field experiment on an actual steel truss bridge whose truss member was artificially severed. The proposed method detected two different damage levels successfully. A damage indicator previously investigated by the authors is also evaluated with respect to the experimental data, and compared with the proposed method.</p>

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