Risk-based pavement maintenance planning considering budget and pavement deterioration uncertainty

Although some parameters are uncertain in pavement Maintenance and Rehabilitation (M&R) planning, most of the literature has considered these parameters deterministic. Likewise, minimizing risk has been an immense concern as a significant amount of money should be spent on the M&R of pavement networks. To this end, this study aims to propose a new approach to consider risk and uncertainty in a pavement M&R optimization problem. A Multistage Stochastic Integer Programming including risk constraints is applied for the problem modeling. Conditional Value at Risk index is applied to minimize the mentioned variables and avoid unacceptable risks. Subsequently, a case study is used to analyze the proposed technique's effectiveness. The results indicate that the total pavement M&R agency cost equals 6.33 billion Tomans considering a confidence level of 95%. Furthermore, the proposed approach can detect the optimal value of the annual budget for pavement network M&R, which is 1.96 billion Tomans for the case study. Applying preventive maintenance can compensate for the negative effects of risk on projects. Therefore, the proposed approach can help decision-makers, policymakers, and transport agencies to analyze and control risk in the pavement M&R optimization problems and determine the optimal M&R budget.