Network-level pavement maintenance and rehabilitation planning considering uncertainties using chance-constrained programming

ABSTRACT

In practice, given limited funds, to consider multiple strategic goals/objectives that different stakeholders concern, pavement network-level maintenance and rehabilitation (M&R) planning becomes a multi-objective optimisation (MOO) based project selection and budget allocation problem. In an attempt to solve this problem, most agencies established MOO models under the deterministic situation without appropriate consideration of uncertainties. However, ignoring performance uncertainties often leads to unreasonable decisions. To provide more convincing and reliable pavement M&R decisions, this paper proposes a Chance-Constrained Programming (CCP) based MOO method to incorporate performance uncertainties in network-level single period pavement M&R planning. First, a general deterministic MOO model with budget and network performance constraints is established. Then, three commonly-used statistical forms of network-level performance measures are introduced. To incorporate uncertainties, the probability distribution of each form of performance measure is derived. Based on the CCP method, the MOO model is transformed to an equivalent deterministic formulation as a mixed non-linear integer programming (MNLIP) problem. To demonstrate the proposed method, a case study using real data is conducted. The results show that the proposed method can effectively help decision-makers to appropriately incorporate performance uncertainties in conducting network-level pavement M&R planning.

KEYWORDS:

• Pavement management
• maintenance and rehabilitation
• multi-objective optimisation
• network-level performance measures
• chance-constrained programming

Acknowledgement

The authors acknowledge the contributions of Mr. Yuqi Shao, Mr. Shuai Wu for their assistance when preparing this paper.

Disclosure statement

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

Data availability statement

Data generated or analysed during the study are available from the corresponding author by request.

Additional information

Funding

The research was supported by the National Natural Science Foundation of China (51308335, 52108395), the Special Funds for Central Administration Guiding Local Science and Technology Development of Shaanxi Province (2019JM-228, 2022JQ-735, 2023-YBSF-248, 2024GX-YBXM-528), the Fundamental Research Funds for the Central Universities of Ministry of Education of China (300102342203; 300102219312; 300102342501; 300102342201), and the 111 project of Sustainable Development of Transportation in Western and Urban Agglomeration [grant numbers B20035].