Addressing the demands for digital governance and sustainable development of transportation assets

the precise monitoring and assessment of asphalt pavement structural health

coupled with predictive maintenance decision-making

represents a critical breakthrough in road engineering operations and maintenance. This study systematically reveals the enabling mechanisms and application boundaries of machine learning technologies in pavement health monitoring: theoretically coupling data-driven paradigms with the nonlinear deterioration characteristics of pavements demonstrates the scientific inevitability of machine learning in achieving precise condition prediction and intelligent assessment. It deconstructs the ontological limitations of traditional monitoring systems in integrating heterogeneous data

modeling pathological succession

and linking maintenance decisions. This analysis proposes a machine learning implementation pathway integrating multi-source sensing

intelligent diagnosis

and closed-loop decision-making

providing theoretical guidance and a technical framework for intelligent infrastructure management.