Engineering-Adaptive Pavement Maintenance Decision-Making Model: A Reinforcement Learning Approach From Expert Feedback

The increase in highway mileage and lifespan is driving up the demand for road maintenance. With most research focusing on corrective maintenance, remedial maintenance(such as sealing and patching) optimization is understudied. Oriented toward remedial maintenance, data-driven models often fall short due to difficulty in establishing and implementing the model under complex road conditions, while the experts’ decision lacks consistency amidst multifaceted factors. To address this gap, this paper proposes a fine-grained maintenance decision model that combines data-driven methods with expert knowledge through Reinforcement Learning from Expert Feedback (RLEF). The experts’ experience introduced in decision-making model could improve the engineering application ability of decisions. The research uses a pavement performance prediction model as the environment and applies reinforcement learning to optimize strategies in the decision model. Additionally, the model integrates multidimensional expert feedback into reward functions to better understand ambiguous decision rules. Real-world data validation demonstrates that the RLEF model can adapt to engineering scenarios and applications better as well as achieve superior cost-effectiveness.