Integrating Probabilistic Pavement Repair Effects for Network-Level Repair Optimization

Effective pavement repair planning is vital for sustaining performance and minimizing lifecycle costs. At the network level, most agencies still rely on deterministic repair-effect assumptions, where repair outcomes are defined by fixed restoration values derived from experience or experimental averages. However, such assumptions often deviate from actual field performance, leading to overestimated repair efficiency and suboptimal investment decisions. This study develops a framework that integrates stochastic repair effects estimated from historical repair data using a probabilistic model for estimating repair effects. The effects of different repairs are represented as probability distributions derived from the latent-variable projection of stochastic deterioration hazard functions, which define the repair transition probabilities. These stochastic transitions are embedded within a Markov Decision Process to optimize the selection of repair types according to condition state, repair effect, cost, and serviceability thresholds, all within a constrained budget. The framework’s application to Addis Ababa’s 150 km urban road network resulted in a five-year optimal strategy that prioritized cost-effective treatments, such as patching, leading to an improvement in network serviceability from 65.7% to 81.2% at a total cost of USD 11.12 million. A comparative analysis of the deterministic restoration approach, commonly used by the agency, overestimated network-level performance by approximately 19%, as it ignored the variability of recovery captured by the stochastic model. Hence, the proposed stochastic framework enables agencies to achieve realistic, data-driven, and sustainable repair optimization, avoiding overestimation of repair benefits while maintaining serviceability within budget constraints.