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

• Published in: Engineering (Beijing, China) Vol. 7; no. 6; pp. 845 - 856
• Main Authors: Hou, Yue, Li, Qiuhan, Zhang, Chen, Lu, Guoyang, Ye, Zhoujing, Chen, Yihan, Wang, Linbing, Cao, Dandan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2021; Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China%Department of Civil and Environmental Engineering The Hong Kong Polytechnic University,Hong Kong 999077,China%National Center for Materials Service Safety,University of Science and Technology Beijing,Beijing 100083,China%Beijing Key Laboratory of Traffic Engineering,Beijing University of Technology,Beijing 100124,China; Beijing Key Laboratory of Traffic Engineering,Beijing University of Technology,Beijing 100124,China%Beijing Key Laboratory of Traffic Engineering,Beijing University of Technology,Beijing 100124,China; School of Transportation,Southeast University,Nanjing 211189,China%Department of Civil and Environmental Engineering,Virginia Tech,Blacksburg,VA 24061,USA; Elsevier
• Subjects: Image processing techniques; Intrusive sensing; Machine learning methods; Pavement monitoring and analysis; The state-of-the-art review; Intrusive sensing; Image processing techniques; Pavement monitoring and analysis; Machine learning methods; The state-of-the-art review
• ISSN: 2095-8099
• DOI: 10.1016/j.eng.2020.07.030

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.