Quantifying the Relative Change in Maintenance Costs due to Delayed Maintenance Actions in Transportation Infrastructure

Abstract

Identifying optimal maintenance policies for transportation infrastructure such as bridges, is a challenging task that requires taking into account many aspects relating to budget availability, resource allocation and traffic rerouting. In practice, it is difficult to accurately quantify all of the aforementioned factors; accordingly, it is equally difficult to obtain network-scale optimal maintenance policies. This paper presents an approach to evaluate the costs associated with deviations from optimal bridge-level maintenance policies, specifically focusing on delays in maintenance actions. Evaluating the cost of maintenance delays is performed using a reinforcement learning (RL) approach that relies on a probabilistic deterioration model to describe the deterioration in the structural components. The RL framework provides estimates for the total expected discounted maintenance costs associated with each maintenance policy over time, allowing comparison of maintenance policies where maintenance actions are delayed against an optimal maintenance policy. The comparisons are performed by probabilistically quantifying the ratio of expected costs associated with each maintenance policy. This ratio represents the trade-offs between performing or delaying maintenance actions over time. Moreover, the proposed approach is scalable, making it applicable to bridges with numerous structural elements. Example of application using the proposed framework is demonstrated using inspection data from bridges in the Quebec province of Canada.