Asphalt pavement maintenance plans intelligent decision model based on reinforcement learning algorithm

Abstract

Making a proper maintenance plan for the pavement health is the key to maintain a good service level and bearing capacity. To cope with the increasing demand for pavement maintenance, based on the idea of reinforcement learning, an intelligent decision-making model for pavement maintenance plans based on proximal policy optimization algorithm was proposed in this paper. The decision model fully considers the comprehensive maintenance benefit-cost ratio during the whole life cycle of the road. To overcome the problems of the experience-led manual decision-making, it conducted the decision-making between pavement conditions and maintenance plans based on data mining technique. Besides, a method for constructing a reinforcement learning Environment module based on a deep artificial neural network was proposed, and a reward function is designed for road maintenance decisions. The model was applied to the highway maintenance decision in Jiangsu Province, and verified that the decision overall accuracy of the reinforcement learning model was 82.2%, which was an increase of 17.2% compared with the artificial neural network model.