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A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach

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  • Chootinan, Piya
  • Chen, Anthony
  • Horrocks, Matthew R.
  • Bolling, Doyt

Abstract

The objective of this paper is to introduce a multi-year pavement maintenance programming methodology that can explicitly account for uncertainty in pavement deterioration. This is accomplished with the development of a simulation-based genetic algorithm (GA) approach that is capable of planning the maintenance activities over a multi-year planning period. A stochastic simulation is used to simulate the uncertainty of future pavement conditions based on the calibrated deterioration model while GA is used to handle the combinatorial nature of the network-level pavement maintenance programming. The effects of the uncertainty of pavement deterioration on the maintenance program are investigated using a case study. The results show that programming the maintenance activities using only the expected pavement conditions is likely to underestimate the required maintenance budget and overestimate the performance of pavement network.

Suggested Citation

  • Chootinan, Piya & Chen, Anthony & Horrocks, Matthew R. & Bolling, Doyt, 2006. "A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(9), pages 725-743, November.
    • Handle: RePEc:eee:transa:v:40:y:2006:i:9:p:725-743
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    References listed on IDEAS

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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. Qiao, Julie Yu & Du, Runjia & Labi, Samuel & Fricker, Jon D. & Sinha, Kumares C., 2021. "Policy implications of standalone timing versus holistic timing of infrastructure interventions: Findings based on pavement surface roughness," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 79-99.
    2. Li, Sirui & Liu, Ying & Wang, Pengfei & Liu, Peng & Meng, Jun, 2020. "A novel approach for predicting urban pavement damage based on facility information: A case study of Beijing, China," Transport Policy, Elsevier, vol. 91(C), pages 26-37.
    3. Seyedshohadaie, S. Reza & Damnjanovic, Ivan & Butenko, Sergiy, 2010. "Risk-based maintenance and rehabilitation decisions for transportation infrastructure networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(4), pages 236-248, May.
    4. 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.
    5. Ohlmann, Jeffrey W. & Bean, James C., 2009. "Resource-constrained management of heterogeneous assets with stochastic deterioration," European Journal of Operational Research, Elsevier, vol. 199(1), pages 198-208, November.
    6. Pantha, Bhoj Raj & Yatabe, Ryuichi & Bhandary, Netra Prakash, 2010. "GIS-based highway maintenance prioritization model: an integrated approach for highway maintenance in Nepal mountains," Journal of Transport Geography, Elsevier, vol. 18(3), pages 426-433.
    7. Sathaye, Nakul & Madanat, Samer, 2012. "A bottom-up optimal pavement resurfacing solution approach for large-scale networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 520-528.

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