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.
关键词
沥青路面/路面养护决策/深度强化学习/养护方案
Key words
asphalt pavement/pavement maintenance decision/deeply reinforcement learning/maintenance plan
维普
万方数据