Abstract

Pavement infrastructure management represents a critical challenge for transportation agencies worldwide, requiring optimal allocation of limited maintenance resources across extensive road networks while ensuring safety, serviceability, and long-term sustainability. Traditional approaches to pavement maintenance and rehabilitation scheduling have predominantly relied on deterministic models, condition-based maintenance strategies, and optimization techniques that often fail to capture the complex, dynamic, and stochastic nature of pavement deterioration processes (Ahmed et al., 2020; Babashamsi et al., 2016). This research presents a novel reinforcement learning framework specifically designed to address the multifaceted challenges inherent in pavement maintenance and rehabilitation decision-making processes.
The proposed reinforcement learning approach leverages advanced machine learning algorithms to develop adaptive maintenance scheduling systems that can learn from historical pavement performance data, environmental conditions, traffic loading patterns, and maintenance intervention outcomes (Elujide et al., 2021; Olamijuwon, 2020). Unlike conventional optimization methods that require extensive prior knowledge of system dynamics and explicit mathematical formulations, the reinforcement learning framework enables autonomous learning and continuous improvement of maintenance strategies through interaction with the pavement management environment. The methodology incorporates multi-objective optimization principles, considering simultaneously the minimization of life-cycle costs, maximization of pavement performance indices, and optimization of network-level service quality metrics.
The research methodology employs a comprehensive data-driven approach, utilizing extensive datasets from multiple transportation agencies, including pavement condition assessments, maintenance histories, traffic volume data, and climatic information. The reinforcement learning model is structured as a Markov Decision Process, where pavement sections represent states, maintenance actions constitute the action space, and reward functions are designed to reflect the complex trade-offs between immediate maintenance costs and long-term performance benefits. Deep Q-learning algorithms, combined with neural network architectures, enable the system to handle high-dimensional state spaces and complex decision scenarios characteristic of real-world pavement management applications.
Computational experiments demonstrate significant improvements in maintenance scheduling efficiency, with the reinforcement learning approach achieving 15-20% reduction in total life-cycle costs compared to traditional optimization methods while maintaining superior pavement condition indices across the network. The framework exhibits remarkable adaptability to varying environmental conditions, traffic patterns, and budget constraints, demonstrating robust performance across diverse geographical regions and infrastructure contexts. These findings suggest substantial potential for transforming current pavement management practices through the integration of advanced artificial intelligence methodologies.