A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction

Jinwoo Lee, Samer Madanat

    Research output: Contribution to journalArticle

    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.

    Original languageEnglish (US)
    Pages (from-to)106-122
    Number of pages17
    JournalTransportation Research Part B: Methodological
    Volume78
    DOIs
    StatePublished - Aug 1 2015

    Fingerprint

    Pavements
    Patient rehabilitation
    rehabilitation
    budget
    reconstruction
    methodology
    Highway systems
    Deterioration
    Genetic algorithms
    Polynomials
    Economics
    history
    economics

    Keywords

    • Bottom-up optimization
    • History-dependent deterioration
    • Multiple constraints
    • Pavement reconstruction
    • Pavement rehabilitation

    ASJC Scopus subject areas

    • Transportation

    Cite this

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    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.",
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