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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 Orcid Logo

Engineering, Volume: 7, Issue: 6, Pages: 845 - 856

Swansea University Author: Yue Hou

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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 t...

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Published in: Engineering
ISSN: 2095-8099 1558-0016
Published: Elsevier BV 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa61799
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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.
Keywords: Pavement monitoring and analysis; The state-of-the-art review; Intrusive sensing; Image processing techniques; Machine learning methods
College: Faculty of Science and Engineering
Funders: This work was supported by the National Key R&D Program of China (2017YFF0205600), the International Research Cooperation Seed Fund of Beijing University of Technology (2018A08), Science and Technology Project of Beijing Municipal Commission of Transport (2018-kjc-01-213), and the Construction of Service Capability of Scientific and Technological Innovation-Municipal Level of Fundamental Research Funds (Scientific Research Categories) of Beijing City (PXM2019_014204_500032).
Issue: 6
Start Page: 845
End Page: 856