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A multi-objective differential evolutionary algorithm for optimal sustainable pavement maintenance plan at the network level

Li, Junda; Pitt, Michael; Ma, Ling; Jia, Jing; Jiang, Feng; (2022) A multi-objective differential evolutionary algorithm for optimal sustainable pavement maintenance plan at the network level. Journal of Cleaner Production , Article 135212. 10.1016/j.jclepro.2022.135212. (In press). Green open access

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Abstract

Sustainable highway pavement maintenance is important for achieving sustainability in the transportation sector. Because the three aspects included in sustainability metrics (environment, economy, and society) often contradict each other, maximising the sustainability performance of highway pavements is difficult, especially at the network level. This study developed a novel multi-objective heuristic algorithm to formulate sustainable highway pavement network maintenance plans considering carbon emissions (CE), life cycle agency cost (LCAC), and pavement long-term performance (LTP). The proposed algorithm is a new variant of multi-objective differential evolution (MODE) that incorporates self-adaptive parameter control and hybrid mutation strategies embedded in its framework (MOSHDE). Three state-of-the-art multi-objective heuristics, namely, the non-dominated sorting genetic algorithm II(NSGA-II), classic MODE, and multi-objective particle swarm optimisation (MOPSO), as well as the proposed MOSHDE, were applied to an existing highway pavement network in China for performance evaluation. Compared with other heuristic algorithms, the proposed self-adaptive parameter control strategy enables the automatic adjustment of the control parameters, avoiding the time-consuming process of selecting them and enhancing the robustness and applicability of differential evolution. The hybrid mutation strategy uses both exploration and exploitation operators for the mutation operations, thus leveraging both global and local searches. The results of the numerical experiment demonstrate that MOSHDE outperforms the other tested heuristics in terms of efficiency and quality and diversity of the obtained approximate Pareto set. The optimal solutions obtained by the proposed method correspond to a proactive maintenance policy, as opposed to the reactive maintenance policy commonly adopted in current practice. In addition, these solutions are more cost-effective and environmentally friendly and can provide better pavement performance to highway users over the project life cycle. Therefore, the proposed MOSHDE may help practitioners in the transportation sector make their highway infrastructure more sustainable.

Type: Article
Title: A multi-objective differential evolutionary algorithm for optimal sustainable pavement maintenance plan at the network level
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jclepro.2022.135212
Publisher version: https://doi.org/10.1016/j.jclepro.2022.135212
Language: English
Additional information: © 2022 Published by Elsevier Ltd. This is an open access article under the CC BY 4.0 license Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)
Keywords: Pavement maintenance, Sustainability, Life cycle management, Multi-objective optimisation, Heuristic algorithm, Differential evolution
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
URI: https://discovery.ucl.ac.uk/id/eprint/10159988
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