A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability

摘要

Current practice adopted by highway agencies with regards to pavement management, has mostly consisted of employing life cycle costs analysis (LCCA) systems to evaluate the overall long-term economic efficiency of competing pavement design and maintenance and rehabilitation (M&R) activities alternatives. This way of supporting the decision-making process as it relates to pavement management, in which little or no importance is given to environmental considerations, suggests the need for pavement management decision-support systems (DSS), which, by integrating multi-disciplinary and complementary pavement life cycle modelling approaches, enable the decision makers (DMs) to properly account for, consider and assess the lifetime impacts of their decisions and practices regarding sustainability goals and targets. This only can be achieved by employing techniques and tools provided with a comprehensive and wide-scoped cradle-to-grave capacity of analysis. To address this multifaceted problem, this paper presents a comprehensive and modular multi-objective optimization (MOO)-based pavement management DSS which comprises three main components: (1) a MOO module; (2) a comprehensive and integrated pavement life cycle costs - life cycle assessment (LCC-LCA) module that covers the whole life cycle of the pavement; and (3) a decision-support module. The potential of the proposed DSS is illustrated with one case study consisting of determining the optimal M&R strategy for a one-way flexible pavement section of a typical Interstate highway in Virginia, USA, which yields the best trade-off between the following three often conflicting objectives: (1) minimization of the present value (PV) of the total life cycle highway agency costs (LCHAC); (2) minimization of the PV of the life cycle road user costs (LCRUC); and (3) minimization of the life cycle greenhouse gas emissions (LCGHG). In comparison to the traditional maintenance strategy, the proposed DSS suggest a maintenance plan that reduces LCHAC by 15%, LCRUC by 28% and LCGHG by 26%.