Multimodal Sensing and Intelligent Diagnostics for Safe and Resilient Transportation Infrastructure

Transportation infrastructure is increasingly challenged by ageing assets, growing traffic demand, climate-related hazards, and complex operating environments. Rails, roads, bridges, tunnels, and associated assets require reliable and timely condition information to maintain safety, serviceability, and network resilience. Recent progress in vision, LiDAR, vibration, acoustic, ultrasonic, electromagnetic, fibre-optic, onboard, and robotic inspection has expanded the ability to observe deterioration. Yet individual sensing methods can provide partial or uncertain evidence, while data from different platforms are often difficult to interpret consistently. There remains a need for integrated diagnostic approaches that transform heterogeneous measurements into reliable knowledge of infrastructure condition and support proactive, resilience-oriented asset management.This Research Topic aims to advance multimodal sensing and intelligent diagnostics for safe, reliable, and climate-resilient transportation infrastructure. It seeks contributions that combine sensing technologies with signal processing, artificial intelligence, physics-informed modelling, data fusion, and uncertainty quantification to improve early damage identification, condition assessment, and defect-severity evaluation. The collection will address how heterogeneous observations can be integrated across inspection platforms and interpreted through engineering knowledge to produce robust, transferable, and actionable diagnostic results. Particular emphasis will be placed on approaches that connect condition intelligence with prognosis, risk assessment, maintenance prioritisation, or decision support. By bringing together infrastructure engineers, sensing specialists, and data-driven modelling researchers, the Research Topic will promote validated methods that improve safety, availability, lifecycle performance, and resilience in real transportation systems.We welcome Original Research, Reviews, Methods, and Perspectives on railway tracks, roads, bridges, tunnels, and other safety-critical transportation assets. Relevant themes include multimodal inspection using vision, LiDAR, vibration, acoustic, ultrasonic, electromagnetic, and fibre-optic sensing; onboard and robotic inspection; intelligent signal and image analysis; cross-modal data fusion; physics-informed machine learning; digital twins; uncertainty-aware diagnosis and prognosis; and maintenance-oriented decision support. Submissions should demonstrate a clear contribution to reliable condition intelligence, diagnostic accuracy, transferability, or resilient asset management. Studies that combine measured data with laboratory, full-scale, field, or operational validation are especially encouraged. Purely generic AI applications without an infrastructure-engineering interpretation or validation are outside the primary scope of this Research Topic. Topic editors: Yang Wang, You DONG, Zhihai Zhang, Mahantesh Nadakatti Source: https://www.frontiersin.org/research-topics/86451/multimodal-sensing-and-intelligent-diagnostics-for-safe-and-resilient-transportation-infrastructure