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

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

81 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)725-743
Number of pages19
JournalTransportation Research Part A: Policy and Practice
Volume40
Issue number9
DOIs
Publication statusPublished - 1 Nov 2006
Externally publishedYes

Keywords

  • Genetic algorithms
  • Pavement maintenance programming
  • Simulation-optimization

ASJC Scopus subject areas

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

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