AI-Driven Predictive Maintenance Frameworks for Smart Bridge and Highway Infrastructure: A Data-Centric Review

Abstract

Transportation infrastructure networks, particularly bridges and highway pavements, represent vital economic arteries that face accelerating structural degradation due to environmental exposure, aging materials, and exponentially increasing traffic volumes. Traditional reactive and schedule-based maintenance approaches suffer from significant operational inefficiencies, high costs, and catastrophic risk exposure. Recent breakthroughs in artificial intelligence (AI), Internet of Things (IoT) sensing, and big data analytics have paved the way for AI-driven predictive maintenance (PdM) paradigms. This paper provides a comprehensive, data-centric review of AI frameworks deployed for structural health monitoring (SHM) and predictive maintenance across smart bridge and highway infrastructure. We systematically examine the data pipeline—spanning multi-modal sensor acquisition (acoustic, vibration, strain, thermal, and UAV imagery), edge-cloud data management, and data preprocessing techniques. We evaluate state-of-the-art machine learning and deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), Transformer-based time-series models, and Physics-Informed Neural Networks (PINNs) designed for defect detection, remaining useful life (RUL) estimation, and structural failure prediction. Furthermore, we analyze major industry challenges such as data sparsity, sensor noise, environmental domain shifts, model interpretability, and system scalability. The review concludes by identifying critical research gaps and proposing future directions toward self-healing infrastructure digital twins and physics-guided autonomous decision-support systems.

KEYWORDS: Predictive Maintenance, Structural Health Monitoring, Deep Learning, Graph Neural Networks, Physics-Informed Neural Networks, Smart Bridges, Highway Infrastructure, Remaining Useful Life (RUL).