Fine Maintenance Decision of Asphalt Pavement based on DDQN Reinforcement Learning
This paper employs a Double Deep Q-Network(DDQN)reinforcement learning approach to analyze pavement maintenance decisions,aiming to maximize the long-term benefit-cost ratio of the pavement.A maintenance decision model is constructed to calculate a more cost-effective maintenance plan.This model uses the number of road segments and years as state features,four maintenance measures as the action space,and the ratio of pavement maintenance benefits to costs as the reward.An action selection strategy is proposed,which ensures that the pavement meets operational requirements.Practical engineering data is used as a case study.The results indicate that the convergence speed of the DDQN-based maintenance decision model is twice as fast as the Deep Q-Network(DQN)model.The calculated maintenance plan demonstrates a higher benefit-cost ratio,keeping the pavement in excellent condition.
asphalt pavementpavement maintenance decisiondeeply reinforcement learningmaintenance plan
万方数据
维普
