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Smart Structures and Systems
  Volume 15, Number 2, February 2015 , pages 395-408

A Bayesian approach for vibration-based long-term bridge monitoring to consider environmental and operational changes
Chul-Woo Kim, Tomoaki Morita, Yoshinobu Oshima and Kunitomo Sugiura

    This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the auto-regressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage.
Key Words
    long-term bridge monitoring; Bayesian regression; temperature; vehicle weight; vibration
Chul-Woo Kim, Tomoaki Morita, Yoshinobu Oshima and Kunitomo Sugiura: Department of Civil and Earth Resources Engineering, Graduate School of Engineering, Kyoto University, Kyotodaigaku Katsura, Nishikyo-ku, Kyoto 615-8540, Japan

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