1 research outputs found
A deep reinforcement learning model for predictive maintenance planning of road assets: Integrating LCA and LCCA
Road maintenance planning is an integral part of road asset management. One
of the main challenges in Maintenance and Rehabilitation (M&R) practices is to
determine maintenance type and timing. This research proposes a framework using
Reinforcement Learning (RL) based on the Long Term Pavement Performance (LTPP)
database to determine the type and timing of M&R practices. A predictive DNN
model is first developed in the proposed algorithm, which serves as the
Environment for the RL algorithm. For the Policy estimation of the RL model,
both DQN and PPO models are developed. However, PPO has been selected in the
end due to better convergence and higher sample efficiency. Indicators used in
this study are International Roughness Index (IRI) and Rutting Depth (RD).
Initially, we considered Cracking Metric (CM) as the third indicator, but it
was then excluded due to the much fewer data compared to other indicators,
which resulted in lower accuracy of the results. Furthermore, in
cost-effectiveness calculation (reward), we considered both the economic and
environmental impacts of M&R treatments. Costs and environmental impacts have
been evaluated with paLATE 2.0 software. Our method is tested on a hypothetical
case study of a six-lane highway with 23 kilometers length located in Texas,
which has a warm and wet climate. The results propose a 20-year M&R plan in
which road condition remains in an excellent condition range. Because the early
state of the road is at a good level of service, there is no need for heavy
maintenance practices in the first years. Later, after heavy M&R actions, there
are several 1-2 years of no need for treatments. All of these show that the
proposed plan has a logical result. Decision-makers and transportation agencies
can use this scheme to conduct better maintenance practices that can prevent
budget waste and, at the same time, minimize the environmental impacts