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A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction
Institution:1. Department of Civil and Environmental Engineering, University of California at Berkeley, 116 McLaughlin Hall, Berkeley, CA 94720-1720, United States;2. Department of Civil and Environmental Engineering, University of California at Berkeley, 763 Davis Hall, Berkeley, CA 94720-1710, United States;1. The Hong Kong Polytechnic University, Hong Kong;2. New York University Abu Dhabi, Abu Dhabi, United Arab Emirates;1. School of Transportation, Southeast University, Nanjing 210096, China;2. Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, United States;1. New York University Abu Dhabi, Abu Dhabi, 129188, United Arab Emirates;2. New York University, Brooklyn, NY, 11201, United States;3. The Hong Kong Polytechnic University, Hong Kong, China;1. The Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology, 193, Munji-ro, Yuseong-gu, Daejeon, 34051, Republic of Korea;2. School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk Gu, Seoul, 02841, Republic of Korea;3. Department of Civil and Urban Engineering, New York University, Brooklyn, NY, 11201, United States;4. Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea;1. New York University, New York, United States;2. The Hong Kong Polytechnic University, Hong Kong, China;3. New York University Abu Dhabi, United Arab Emirates
Abstract:We present a methodology for the joint optimization of rehabilitation and reconstruction activities for heterogeneous pavement systems under multiple budget constraints. The proposed bottom-up approach adopts an augmented condition state to account for the history-dependent properties of pavement deterioration, and solves for steady-state policies for an infinite horizon. Genetic algorithms (GAs) are implemented in the system-level optimization based on segment-specific optimization results. The complexity of the proposed algorithm is polynomial in the size of the system and the policy-related parameters. We provide graphical presentations of the optimal solutions for various budget situations. As a case study, a subset of California’s highway system is analyzed. The case study results demonstrate the economic benefit of a combined rehabilitation and reconstruction budget compared to separate budgets.
Keywords:Bottom-up optimization  Pavement reconstruction  Pavement rehabilitation  History-dependent deterioration  Multiple constraints
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