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Incorporating inspection decisions in pavement management

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  • Madanat, Samer

Abstract

Pavement management systems need to address not only maintenance and rehabilitation (M&R) decisions, but also facility inspection decisions. The state of the art in pavement management is lacking of any consistent methodology for making such decisions on a cost-effectiveness basis. Such a methodology must recognize the presence of interactions between M&R and inspection decisions. These interactions argue for a joint decision-making approach, where the sum of inspection and M&R costs is minimized. This paper reviews different possible mathematical formulations to such a joint decision-making model, having various levels of restriction and computational complexity. These formulations are then compared and the effect of the forecast uncertainty on the minimum expected costs produced by each of them is investigated empirically. It is concluded that optimizing inspection decisions jointly with M&R decisions can lead to substantial cost savings, especially for high precisions of forecasting.

Suggested Citation

  • Madanat, Samer, 1993. "Incorporating inspection decisions in pavement management," Transportation Research Part B: Methodological, Elsevier, vol. 27(6), pages 425-438, December.
    • Handle: RePEc:eee:transb:v:27:y:1993:i:6:p:425-438
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    Citations

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    Cited by:

    1. Prozzi, Jorge A, 2001. "Modeling Pavement Performance by Combining Field and Experimental Data," University of California Transportation Center, Working Papers qt1gx2425x, University of California Transportation Center.
    2. Prozzi, J A & Madanat, S M, 2004. "Development of Pavement Performance Models by Combining Experimental and Field Data," University of California Transportation Center, Working Papers qt6cf8v5cw, University of California Transportation Center.
    3. Lee, Jinwoo & Madanat, Samer, 2015. "A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 106-122.
    4. Dunja Perić & Gyuhyeong Goh & Javad Saeidaskari & Arash Saeidi Rashk Olia & Pooyan Ayar, 2022. "Development of Prediction Models for Performance of Flexible Pavements in Kansas with Emphasis on the Effects of Subgrade and Unbound Layers," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    5. Gu, Weihua & Ouyang, Yanfeng & Madanat, Samer, 2012. "Joint optimization of pavement maintenance and resurfacing planning," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 511-519.
    6. Madanat, S M & Park, Sejung & Kuhn, K D, 2006. "Adaptive Optimization and Systematic Probing of Infrastructure System Maintenance Policies under Model Uncertainty," University of California Transportation Center, Working Papers qt4fb7k5rc, University of California Transportation Center.
    7. Kobayashi, Kiyoshi & Kaito, Kiyoyuki & Lethanh, Nam, 2012. "A statistical deterioration forecasting method using hidden Markov model for infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 544-561.
    8. Zhang, Le & Fu, Liangliang & Gu, Weihua & Ouyang, Yanfeng & Hu, Yaohua, 2017. "A general iterative approach for the system-level joint optimization of pavement maintenance, rehabilitation, and reconstruction planning," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 378-400.
    9. Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 202-213.
    10. Seites-Rundlett, William & Bashar, Mohammad Z. & Torres-Machi, Cristina & Corotis, Ross B., 2022. "Combined evidence model to enhance pavement condition prediction from highly uncertain sensor data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

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