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Application of Bayesian estimation method with Markov hazard model to improve deterioration forecasts for infrastructure asset management

Abstract

The heart of asset management systems for road infrastructure is the deterioration forecasting model. It provides the most fundamental information for better asset management. So far, there are many practices to build a reliable forecasting model using inspection data in conjunction with statistical theories. In many applications, however, an inadequate stock of inspection data or difficulty in applying sophisticated statistical methods have often been serious obstacles. As a solution, this paper suggests applying the Bayesian estimation method with a multi-state exponential hazard Markov chain model for simple and reliable deterioration forecasting for infrastructure. The main contents of this paper are an introduction of the model’s framework combining Markov chain, hazard theory, and Bayesian estimation method, and a demonstration of its practical application with an empirical study. The empirical study was conducted with time-series inspection data of pavement from the Korean National Highways. The estimation results from the suggested method would be useful for improving the current pavement maintenance strategy for Korean National Highways. However, the most important message of this paper is that the framework of the Bayesian Markov hazard model could be the best model to use for other civil infrastructure that has gradual changes in condition. The great advantages from the Bayesian estimation method may facilitate development of customized asset management systems.

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Correspondence to Daeseok Han.

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Han, D., Kaito, K. & Kobayashi, K. Application of Bayesian estimation method with Markov hazard model to improve deterioration forecasts for infrastructure asset management. KSCE J Civ Eng 18, 2107–2119 (2014). https://doi.org/10.1007/s12205-012-0070-6

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  • DOI: https://doi.org/10.1007/s12205-012-0070-6

Keywords

  • asset management
  • pavement deterioration forecasting
  • Bayesian estimation method
  • Markov chain
  • Monte Carlo simulation
  • Bayesian Markov hazard model
  • Korean national highways