Reinforcement learning in the maintenance of civil infrastructures

Shiyin Wei, Xiaowei Jin, Hui Li

Keywords: Bridge maintenance policy, Deep Reinforcement Learning (DRL), Markov Decision Process (MDP), Deep Q-network (DQN), Convolutional Neural Network (CNN)

Abstract:

Life-cycle management of aged civil infrastructures is
an issue of worldwide concern. The process of sequential
decision making on structural maintenance is usually
considered as a Markov Decision Process (MDP) where
Markov property holds in the structural condition transition
due to the deterioration and maintenance. However, policy-making
for large MDPs for maintenance of complex realistic
infrastructures has long been a challenging problem
due to the high-dimensions. Thus we introduce a deep reinforcement
learning(DRL) framework to make this available,
and a deep Q network implemented by CNN is employed to
approximate the state-action value in the high-dimensional
state-action space. A maintenance task of a cable-stayed
bridge is designed and used to verify the efficiency of the
proposed approach. The results show that the DRL is effective
and efficient at the policy-making for maintenance
tasks of complex civil infrastructures with high-dimensional
state-action space.