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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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DOI (Published version): 10.1016/j.eng.2020.07.030

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
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URI: https://cronfa.swan.ac.uk/Record/cronfa61799
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last_indexed 2023-01-13T19:22:48Z
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spelling 2022-11-28T15:51:05.7538167 v2 61799 2022-11-07 The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis 92bf566c65343cb3ee04ad963eacf31b Yue Hou Yue Hou true false 2022-11-07 CIVL 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. Journal Article Engineering 7 6 845 856 Elsevier BV 2095-8099 1558-0016 Pavement monitoring and analysis; The state-of-the-art review; Intrusive sensing; Image processing techniques; Machine learning methods 1 6 2021 2021-06-01 10.1016/j.eng.2020.07.030 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 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). 2022-11-28T15:51:05.7538167 2022-11-07T19:24:44.7514559 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Yue Hou 1 Qiuhan Li 2 Chen Zhang 3 Guoyang Lu 4 Zhoujing Ye 5 Yihan Chen 6 Linbing Wang 7 Dandan Cao 0000-0002-4277-5942 8 61799__25939__ed4bd6d957ff4fd090e2eec7d1c02a51.pdf 61799.pdf 2022-11-28T15:48:20.3360805 Output 1233507 application/pdf Version of Record true Copyright 2021 The Authors. This is an open access article under the CC BY-NC-ND license true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis
spellingShingle 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
title_short The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis
title_full The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis
title_fullStr The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis
title_full_unstemmed The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis
title_sort The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Yue Hou
Qiuhan Li
Chen Zhang
Guoyang Lu
Zhoujing Ye
Yihan Chen
Linbing Wang
Dandan Cao
format Journal article
container_title Engineering
container_volume 7
container_issue 6
container_start_page 845
publishDate 2021
institution Swansea University
issn 2095-8099
1558-0016
doi_str_mv 10.1016/j.eng.2020.07.030
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
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description 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.
published_date 2021-06-01T04:20:54Z
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score 11.013619

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