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Developing A Semi-Markov Process Model for Bridge Deterioration Prediction in Shanghai

Author

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  • Yu Fang

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China)

  • Lijun Sun

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China)

Abstract

The performance of urban bridges will deteriorate gradually throughout service life. Bridge deterioration prediction is essential for bridge management, especially for maintenance planning and decision-making. By considering the time-dependent reliability in the bridge deterioration process, a Weibull distribution based semi-Markov process model for urban bridge deterioration prediction was proposed in this paper. Historical inspection records stored in the Bridge Manage System (BMS) database in Shanghai since 2004 were investigated. The Weibull distribution was used to characterize the bridge deterioration behavior within each condition rating (CR), and the semi-Markov process was used to calculate the bridge transition probabilities between adjacent CRs. After that, the service life expectancy of urban bridges, the transition probabilities of the deck system and the substructure, and the future CR proportion change caused by deterioration was predicted. The prediction results indicate that the life expectancy of concrete beam bridges is about 77 years. The decay rate of the deck system is the fastest among three major parts, and the substructure has a much longer life expectancy. It suggests that the overall prediction accuracy of the semi-Markov model in network-level is better than the regression analysis method. Furthermore, the proportion of bridges in intact condition will gradually decrease in the next few decades, while the percentage of bridges in the qualified and bad state will increase rapidly. The prediction results show a good agreement with the actual deterioration trend of the urban bridges in Shanghai. In order to alleviate the pressure of bridge maintenance in the future, it is necessary to adopt a more targeted preventive maintenance strategy.

Suggested Citation

  • Yu Fang & Lijun Sun, 2019. "Developing A Semi-Markov Process Model for Bridge Deterioration Prediction in Shanghai," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5524-:d:273926
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    References listed on IDEAS

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    1. Gianfranco Corradi & Jacques Janssen & Raimondo Manca, 2004. "Numerical Treatment of Homogeneous Semi-Markov Processes in Transient Case–a Straightforward Approach," Methodology and Computing in Applied Probability, Springer, vol. 6(2), pages 233-246, June.
    2. Kamal Golabi & Richard Shepard, 1997. "Pontis: A System for Maintenance Optimization and Improvement of US Bridge Networks," Interfaces, INFORMS, vol. 27(1), pages 71-88, February.
    3. R Medjoudj & D Aissani & A Boubakeur & K D Haim, 2009. "Interruption modelling in electrical power distribution systems using the Weibull—Markov model," Journal of Risk and Reliability, , vol. 223(2), pages 145-157, June.
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    Cited by:

    1. Xu, Gaowei & Azhari, Fae, 2022. "Data-driven optimization of repair schemes and inspection intervals for highway bridges," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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