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

Yue Hou, Qiuhan Li, Chen Zhang, Guoyang Lu, Zhoujing Ye, Yihan Chen, Linbing Wang, Dandan Cao

Research output: Journal article publicationReview articleAcademic researchpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
JournalEngineering
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Image processing techniques
  • Intrusive sensing
  • Machine learning methods
  • Pavement monitoring and analysis
  • The state-of-the-art review

ASJC Scopus subject areas

  • Computer Science(all)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Materials Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Engineering(all)

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