Condition-based pavement management systems accounting for model uncertainty and facility heterogeneity with belief updates

Le Zhang, Weihua Gu, Young Ji Byon, Jinwoo Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Due to the tighter budget for pavement management, schedules of inspection activities should be jointly optimized with the maintenance and reconstruction (M&R) plans for pavement systems. Conducting inspections every year is unnecessary and will decrease the budget for M&R activities, while infrequent inspections may lead to suboptimal M&R planning due to the lack of accurate information. This paper presents a methodology for jointly optimizing the inspection scheduling and M&R planning for pavement systems, considering model uncertainty and facility-specific heterogeneity. The problem is defined as a Partially Observable Markov Decision Process (POMDP) model, accounting for the tradeoff between the information value and inspection costs. Moreover, a statistical learning method is used to update the prediction of pavement conditions using the collected inspection data and, eventually, improve the condition-based decisions. This “belief update” process can gradually reduce the model uncertainty as the dataset size increases. We demonstrate the proposed stochastic optimization framework through a numerical example with a system of fifty heterogenous pavement facilities under a combined budget for inspection and M&R activities. Several managerial insights and implications are discussed. For example, the optimal inspection frequencies are less sensitive to the budget; and the agency should perform fewer reconstructions and more rehabilitations when the budget is limited.

Original languageEnglish
Article number104054
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - Mar 2023


  • Belief update
  • Heterogeneity
  • Inspection
  • M&R
  • Pavement management systems
  • Uncertainty

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Management Science and Operations Research


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