Pavement Decision Intelligence: From Sensing and Physics to Practice

Road networks are critical public assets that must be monitored, modelled, and maintained with rigor and efficiency. As agencies face heavier traffic, climate stressors, and constrained budgets, the central challenge is to convert rich sensing and sound physics into decision-ready knowledge—for both project and network scales. Beyond laser/vision-based pavement inspection—such as Laser Crack Measurement System (LCMS) and three-dimensional laser line scanning, high-resolution visible-spectrum imaging, uncrewed aerial vehicle (UAV) photogrammetry, and light detection and ranging (LiDAR)—today’s practice increasingly leverages in-car road-monitoring telemetry (ROMO) from controller area network (CAN)/on-board diagnostics (OBD), inertial measurement units (IMU), global navigation satellite systems (GNSS), and cameras; skid-resistance measurements such as the Sideway-force Coefficient Method (SKM)/Sideway-force Coefficient Routine Investigation Machine (SCRIM); weigh-in-motion (WIM) load spectra; portable seismic/ultrasonic methods such as the Portable Seismic Property Analyzer (PSPA); deflection-based structural evaluation such as the Traffic Speed Deflectometer (TSD) and Falling Weight Deflectometer (FWD); ground penetrating radar (GPR); embedded glass-fibre sensing; and climatic and winter-maintenance information.In parallel, multi-scale and multi-physics modelling (FEM/DEM; mechanistic–empirical frameworks), physics-informed and hybrid learning (PINN/PIML), computer vision and machine learning, and data assimilation now provide principled bridges from observations to mechanisms. When embedded in uncertainty quantification (UQ) and verification & validation (V&V), and coupled with spatiotemporal alignment, multi-sensor calibration, and external validation across devices/regions, these elements support maintenance timing and method selection, network-level prioritization, and the propagation of uncertainty into LCCA/LCA, resilience, and risk under shifting climates. This Research Topic seeks contributions that make inspection outputs and models trustworthy, transferable, and decision-centric, bridging sensing, modelling, and asset management communities to advance pavement decision intelligence across project and network scales.We welcome (but are not limited to) submissions on:(1) Sensing & Operations Data: LCMS/3D laser, vision/IR imaging, UAV photogrammetry, LiDAR, ROMO in-car telemetry, skid resistance (SKM/SCRIM), WIM, TSD/FWD deflection, GPR, glass-fibre sensing, PSPA, multi-sensor calibration, automated distress quantification;(2) Modelling & Simulation: multi-scale/multi-physics modelling (FEM/DEM; mechanistic–empirical), parameter inference, simulation-to-reality transfer;(3) Physics-Guided Data Intelligence: PINN/PIML, PGML, data assimilation, domain adaptation, foundation/transformer models;(4) Climate & Winter Context: temperature/precipitation, freeze–thaw cycles, winter operations, resilience analysis;(5) Credibility & Transferability: Bayesian calibration, error propagation, cross-device/region validation, reproducible workflows;(6) Decision Pipelines: maintenance timing and treatment selection, network prioritization, integration with LCCA/LCA, field implementations with agency workflows.We encourage original research, review articles, and case studies demonstrating practical deployment. Topic editors: Peng Lin, Dirk Jansen, Linbing Wang, Ruxin Jing, Xueyan Liu, Rutger Krans Source: https://www.frontiersin.org/research-topics/75345/pavement-decision-intelligence-from-sensing-and-physics-to-practice