Planning maintenance and rehabilitation activities for airport pavements: A combined supervised machine learning and reinforcement learning approach

Abstract

Maintenance and Rehabilitation (M&R) of airport pavement assets involves considerable financial resources. As such, even modest improvement in M&R activity planning could lead to non-trivial savings. The state-of-the-practice for planning M&R activities mostly relies on condition thresholds and engineering rules, while the state-of-the-art often requires untested assumptions and relies critically on complete knowledge about pavement deterioration. This study proposes a machine learning (ML)-based approach that integrates pavement condition prediction using supervised ML with M&R activity planning empowered by reinforcement learning (RL), for which the Q-learning method is adopted. Supervised ML involves minimum a priori assumptions while characterizing the relationship between pavement deterioration and its influencing factors, and can yield higher-accuracy prediction of future pavement conditions than traditional methods. Similarly, RL learns the optimal action-value functions in a model-free environment. The integrated ML approach is implemented and demonstrated using real-world data from Chicago O’Hare International Airport. The results show the effectiveness of the proposed approach which can lead to reduced M&R cost compared to practice.