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Deterioration forecasting of joint members based on long-term monitoring data

  • Kiyoshi Kobayashi
  • Kiyoyuki KaitoEmail author
  • Kosuke Kazumi
Research Paper

Abstract

To compensate for shortcomings of visual inspection data- based asset management, monitoring data-based asset management has attracted a lot of attention. However, there are few researches to detect abnormalities and extract the progress of deterioration based on long-term monitoring data. In this study, the authors express the time series data obtained through long-term monitoring by the autoregressive moving average with exogenous variables generalized autoregressive conditional heteroskedasticity (ARMAX-GARCH) model, and develop the efficient method to estimate unknown parameters based on Bayesian method. Then, a method to forecast the timing of detailed inspection utilizing the ARMAX-GARCH model is developed. Lastly, this methodology is applied to the data of long-term monitoring targeted at the joint members of viaduct, to evaluate its effectiveness.

Keywords

ARMAX-GARCH model Long-term monitoring Time series analysis Joint member 

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Copyright information

© Springer-Verlag Berlin Heidelberg and EURO - The Association of European Operational Research Societies 2014

Authors and Affiliations

  • Kiyoshi Kobayashi
    • 1
  • Kiyoyuki Kaito
    • 2
    Email author
  • Kosuke Kazumi
    • 2
  1. 1.Graduate School of ManagementKyoto UniversityKyotoJapan
  2. 2.Department of Civil EngineeringOsaka UniversitySuitaJapan

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