From Icing Detection to Icing Intelligence: Toward Self-Aware and Autonomous Cold-Region Transportation Infrastructure

Icing poses a severe threat to the safety, durability, and operational reliability of transportation infrastructure in cold regions. Existing studies have mainly focused on ice detection, icing prediction, and deicing technologies, while the links between icing evolution, infrastructure response, risk diagnosis, and autonomous regulation remain insufficiently integrated. This review establishes an integrated framework from icing evolution to intelligent perception, state cognition, risk diagnosis, and autonomous regulation. The multiphysics evolution of pavement icing is discussed in terms of ice nucleation, growth, interfacial adhesion, and freeze–thaw-induced degradation. The effects of temperature, moisture, surface characteristics, and traffic loading are considered. Then, multiscale icing perception is reviewed, with emphasis on environmental parameters, vehicle–pavement mechanical responses, and distributed self-powered sensing based on triboelectric, piezoelectric, and other mechanisms. Data-driven models, physics-informed learning, and digital twins are examined for icing-state recognition and future-state prediction. Icing risks are classified into mechanical safety, infrastructure durability, and traffic operation, providing a quantitative link between icing states and engineering consequences. Adaptive mitigation is discussed through passive surface and thermal regulation, active energy-efficient heating, and closed-loop control. Finally, five research directions are proposed, including self-powered icing intelligence, icing foundation models, infrastructure-scale sensing networks, digital twins of icing evolution, and self-adaptive roads. This review provides a unified perspective for the development of self-aware and autonomous ice-resilient transportation infrastructure.