Artificial Intelligence-Driven Asset Management for Cyber-resilient and High-Performance Transportation Infrastructure: A Critical Narrative Review

Abstract

Transportation agencies are being asked to sustain ageing road, bridge and rail networks under tightening budgets, rising traffic loads and a changing climate, and artificial intelligence is increasingly presented as the mechanism through which asset management will become predictive rather than reactive. At the same time, the sensing, communication and computation layers that make such prediction possible convert physical assets into cyber-physical systems whose availability now depends on the integrity of data and models. This review critically examines the evidence supporting artificial intelligence-driven asset management frameworks for transportation infrastructure, and evaluates how far those frameworks address cyber-resilience rather than assuming it. Literature was identified through open scholarly indexes and citation searching, with bibliographic verification of every retained source, and was appraised for methodological adequacy, evidentiary strength and relevance rather than citation count alone. Three findings dominate the synthesis. First, the perception and prediction literature is technically mature but rests on condition data whose labelling reliability is itself contested, which places an unacknowledged ceiling on reported accuracy and undermines cross-study comparison. Second, sequential decision models, particularly those based on deep reinforcement learning, produce policies whose apparent superiority is conditional on simulated deterioration dynamics that have rarely been validated against observed network behaviour. Third, the cybersecurity evidence base concentrates on vehicles, communication channels and perception models, leaving the asset management pipeline itself, from field sensing through data platforms to maintenance programming, largely unexamined as an attack surface. Frameworks that claim to unify performance and resilience typically do so architecturally, without demonstrating that security controls preserve decision quality or that decision optimisation preserves security. The central unresolved question is therefore not whether artificial intelligence can improve asset management, but whether its benefits survive adversarial conditions, degraded data and institutional constraints. Research priorities include reliability-aware benchmarking, field validation of learned maintenance policies, transport-specific threat modelling of asset data pipelines, and joint evaluation of performance and resilience objectives.