Review Article

AI-Driven Predictive Maintenance for Aging Infrastructure: Challenges, Reliability Gaps, and Smart Monitoring Frameworks

Abstract

Aging infrastructure systems, including bridges, pipelines, power grids, and water distribution networks, are deteriorating at a pace that increasingly exceeds available rehabilitation and replacement capacities, creating an urgent need for intelligent maintenance strategies. Although advances in artificial intelligence (AI), machine learning, and Internet of Things (IoT) sensing technologies have accelerated interest in predictive maintenance (PdM), realworld deployment across aging infrastructure environments remains fragmented,  unreliable, and difficult to scale. Existing literature largely emphasizes algorithmic performance under idealized data conditions or isolated case-specific implementations, while insufficiently addressing the systemic barriers that constrain operational adoption. This review introduces the Reliability-Readiness Gap (RRG) as a diagnostic framework describing the mismatch between AI data requirements and the realities of aging infrastructure ecosystems. To address this challenge, the paper further proposes the Stratified Infrastructure Intelligence Framework (SIIF), a staged pathway that aligns AI complexity with infrastructure data maturity and governance readiness. Drawing evidence from transportation, water, energy, and pipeline sectors across developed and developing contexts, the review demonstrates that the primary limitation to AI-PdM scalability is not model sophistication, but the immaturity of infrastructure data ecosystems. Effective deployment, therefore, requires co-development of sensing infrastructure, governance systems, and AI architectures rather than isolated algorithmic advancement.