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History-Dependent Optimization of Bridge Maintenance and Replacement Decisions Using Markov Decision Process

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  • Robelin, Charles-Antoine
  • Madanat, Samer M

Abstract

Bridge maintenance and replacement optimization methods use deterioration models to predict the future condition of bridge components. The purpose of this paper is to develop a framework for bridge maintenance optimization using a deterioration model that takes into account aspects of the history of the bridge condition and maintenance, while allowing the use of efficient optimization techniques. Markovian models are widely used to represent bridge component deterioration. In existing Markovian models, the state is the bridge component condition, and the history of the condition is not taken into account, which is seen as a limitation. This paper describes a method to formulate a realistic history-dependent model of bridge deck deterioration as a Markov chain, while retaining aspects of the history of deterioration and maintenance as part of the model. This model is then used to formulate and solve a reliability-based bridge maintenance optimization problem as a Markov decision process. A parametric study is conducted to compare the policies obtained in this research with policies derived using a simpler Markovian model.

Suggested Citation

  • Robelin, Charles-Antoine & Madanat, Samer M, 2007. "History-Dependent Optimization of Bridge Maintenance and Replacement Decisions Using Markov Decision Process," University of California Transportation Center, Working Papers qt6c94v984, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt6c94v984
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    1. Durango, Pablo L. & Madanat, Samer M., 2002. "Optimal maintenance and repair policies in infrastructure management under uncertain facility deterioration rates: an adaptive control approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(9), pages 763-778, November.
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    Cited by:

    1. Orcesi, André D. & Cremona, Christian F., 2010. "A bridge network maintenance framework for Pareto optimization of stakeholders/users costs," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1230-1243.
    2. Rafic Faddoul & Wassim Raphael & Abdul-Hamid Soubra & Alaa Chateauneuf, 2013. "Incorporating Bayesian networks in Markov Decision Processes," Post-Print hal-01006963, HAL.
    3. Yingnan Yang & Hongming Xie, 2021. "Determination of Optimal MR&R Strategy and Inspection Intervals to Support Infrastructure Maintenance Decision Making," Sustainability, MDPI, vol. 13(5), pages 1-10, March.

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