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A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach
Affiliation:1. Department of Civil and Environmental Engineering, Utah State University, 4110 Old Main Hill, Logan, UT 84322-4110, USA;2. Horrocks Engineers, American Fork, UT 84003, USA;3. Utah Local Technology Assistance Program, Utah State University, Logan, UT 84322-4110, USA;1. Department of Industrial and Systems Engineering, University of Oklahoma, USA;2. School of Civil Engineering and Environmental Science, University of Oklahoma, USA;1. School of Transportation, Southeast University, Nanjing 211189, China;2. National Engineering Laboratory of Highway Maintenance Technology, Changsha University of Science & Technology, Changsha, Hunan 410114, China;1. IFSTTAR, AME-EASE, Route de Bouaye, CS4, F-44341, Bouguenais, France;2. Road Pavements Laboratory, Research Center for Territory, Transports and Environment, Department of Civil Engineering, University of Coimbra, Rua Luís Reis Santos, 3030-788, Coimbra, Portugal;3. Center for Sustainable Transportation Infrastructure, Virginia Tech Transportation Institute, The Charles Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, 3500 Transportation Research Plaza, Blacksburg, VA, 24061, USA;1. Ondokuz Mayis University, Department of Civil Engineering, Samsun, Turkey;2. Istanbul University, Department of Civil Engineering, Istanbul, Turkey;1. Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, USA;2. Departamento de Obras Civiles, Universidad Técnica Federico Santa María, Chile;3. Construction Engineering and Management Department, Engineering School, Pontificia Universidad Catolica de Chile, Chile;4. National Research Center for Integrated Natural Disaster Management, CONICYT/FONDAP/15110017, Chile;5. Department of Civil Engineering, University of Waterloo, Canada
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.
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