Optimisation method of network level pavement maintenance planning based on benefit maximisation subject to budget constraints and a case study

ABSTRACT

Considering that the decision-making and planning of large-scale highway network under the condition of limited funds, there are some problems such as complex algorithm of feasible solution of maintenance scheme and low efficiency of optimisation process, a heuristic maintenance planning optimisation algorithm based on budget is introduced. This paper focuses on the performance prediction, the determination of treatment measures and the optimisation of the optimal scheme. Firstly, through the analysis of the technical process of prediction and treatment measures, the pavement performance degradation models of pavement condition index, riding quality index and rut depth index based on treatment level are established. Then, based on the characteristics of pavement performance degradation in China, the concept of maintenance benefit based on pavement performance index is proposed, and the cost-benefit optimisation model of highway asphalt pavement is established. A budget-based heuristic algorithm is proposed to solve the optimisation model for maintenance planning with budget constraints. Finally, taking Lian-Xu highway network in a case study, the optimal scheme of treatment measures and fund allocation for the maintenance section of the large-scale highway network is realised under the condition of limited funds, so as to verify the feasibility of the optimisation model.

KEYWORDS:

• Highway pavement maintenance
• maintenance planning model
• maintenance benefit
• optimisation scheme
• heuristic optimisation algorithm

Acknowledgments

Firstly, the research activities described in this paper was sponsored in part by National Key Research and Development Program of China (Grant No. 2021YFB 2601200). All the sponsorships are gratefully acknowledged.

In addition, I would like to give my heartfelt thanks to Chen Tao, one of my colleagues, who provided huge amounts of real and valuable data from the Lian-Xu network-level detection during the case study. I am also grateful for another colleague, Zhang Lili, who assisted me in the calculation and application of the model. I want to extend my gratitude to the team in HELLER Co. Ltd, a German company, for the useful guidance and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).