Pavement maintenance planning using a risk-based approach and fault tree analysis

Optimal planning of Maintenance and Rehabilitation (M&R) treatments due to limited resources and funding is vital. A pavement performance function formula is needed to model the M&R optimisation problem. By considering important features, a pavement deterioration function was developed with an R2 of 0.897. The essence of pavement deterioration is uncertain. Hence, disregarding the uncertainty may result in sub-optimal solutions. Therefore, two approaches were employed to optimise M&R treatments in a large-scale pavement network, including deterministic and risk-based. Both approaches aimed to minimise the required budget in the planning period constrained to acceptable pavement condition. A novel and robust metaheuristic algorithm was adjusted to solve the M&R planning problem deterministically. In the risk-based method, appropriate probability distribution functions were fitted to historical data of uncertain parameters. Monte Carlo simulation was performed to generate probability distributions of the required budget and condition of pavement sections. By using a risk-based strategy instead of the deterministic one, the required budget was reduced by 30%. Fault tree analysis was performed on the solutions obtained from the risk-based method to increase the pavement network's reliability. The results of the mentioned analysis represented that only by spending 2.5% more budget network's reliability could be significantly increased.