The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

TLDR

Pavement infrastructure is critical for transportation safety, and monitoring its health using advanced intrusive sensing, image processing, and machine learning is essential to detect damage early and guide maintenance. This review surveys recent advances in intrusive sensing, image processing, and machine learning for pavement monitoring and proposes future research directions. The authors compile and analyze recent literature on intrusive sensing, image processing, and machine learning in pavement engineering, identifying trends and proposing future research pathways.

Abstract

In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.