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Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method

Hui Yao, Shuo Pan, Yaning Fan, Yanhao Liu, Gordon Airey, Anand Sreeram, Yue Hou Orcid Logo

Computer-Aided Civil and Infrastructure Engineering

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1111/mice.70121

Abstract

Detection of subsurface road targets is a crucial task in road engineering. This study focuses on detecting three types of subsurface targets: looseness, pipeline, and voids. Ground-penetrating radar (GPR) was employed to acquire real-world data. gprMax was utilized to generate additional data to ad...

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Published in: Computer-Aided Civil and Infrastructure Engineering
ISSN: 1093-9687 1467-8667
Published: Wiley 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70762
first_indexed 2025-10-23T13:35:46Z
last_indexed 2025-11-22T05:31:59Z
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spelling 2025-11-21T13:25:49.7466542 v2 70762 2025-10-23 Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2025-10-23 ACEM Detection of subsurface road targets is a crucial task in road engineering. This study focuses on detecting three types of subsurface targets: looseness, pipeline, and voids. Ground-penetrating radar (GPR) was employed to acquire real-world data. gprMax was utilized to generate additional data to address the scarcity of the original dataset. Recognizing the substantial disparity between directly simulated gprMax data and actual GPR images, this paper introduces a novel method for synthesizing gprMax-generated data with real measurements, thereby achieving effective GPR image augmentation. Furthermore, a generative adversarial network (GAN) was employed to rapidly produce large volumes of GPR images. Deep learning models were implemented to detect subsurface road targets using datasets of varying scales. Experimental results indicate that data augmentation utilizing gprMax and GAN can substantially improve the detection accuracy for subsurface road targets, achieving a rate of 0.767. This represents a 21.2% enhancement, compared to the results obtained from training on the original dataset. The findings of this research hold practical significance for supporting road maintenance operations. Journal Article Computer-Aided Civil and Infrastructure Engineering 0 Wiley 1093-9687 1467-8667 3 11 2025 2025-11-03 10.1111/mice.70121 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) Hunan Expressway Group Co. Ltd.. Grant Number: 202152; Hunan Department of Transportation. Grant Number: 202152; Key Scientific Research Projects of BBMG Corporation. Grant Number: KYJC018 2025-11-21T13:25:49.7466542 2025-10-23T14:32:15.3392734 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Hui Yao 1 Shuo Pan 2 Yaning Fan 3 Yanhao Liu 4 Gordon Airey 5 Anand Sreeram 6 Yue Hou 0000-0002-4334-2620 7 70762__35594__9fad9a8574b0480ab348b6c7620a4353.pdf 70762.VOR.pdf 2025-11-11T11:19:52.4069973 Output 1013900 application/pdf Version of Record true © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method
spellingShingle Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method
Yue Hou
title_short Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method
title_full Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method
title_fullStr Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method
title_full_unstemmed Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method
title_sort Intelligent detection of subsurface road targets using a combined numerical simulation and deep learning method
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Hui Yao
Shuo Pan
Yaning Fan
Yanhao Liu
Gordon Airey
Anand Sreeram
Yue Hou
format Journal article
container_title Computer-Aided Civil and Infrastructure Engineering
container_volume 0
publishDate 2025
institution Swansea University
issn 1093-9687
1467-8667
doi_str_mv 10.1111/mice.70121
publisher Wiley
college_str Faculty of Science and Engineering
hierarchytype
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
active_str 0
description Detection of subsurface road targets is a crucial task in road engineering. This study focuses on detecting three types of subsurface targets: looseness, pipeline, and voids. Ground-penetrating radar (GPR) was employed to acquire real-world data. gprMax was utilized to generate additional data to address the scarcity of the original dataset. Recognizing the substantial disparity between directly simulated gprMax data and actual GPR images, this paper introduces a novel method for synthesizing gprMax-generated data with real measurements, thereby achieving effective GPR image augmentation. Furthermore, a generative adversarial network (GAN) was employed to rapidly produce large volumes of GPR images. Deep learning models were implemented to detect subsurface road targets using datasets of varying scales. Experimental results indicate that data augmentation utilizing gprMax and GAN can substantially improve the detection accuracy for subsurface road targets, achieving a rate of 0.767. This represents a 21.2% enhancement, compared to the results obtained from training on the original dataset. The findings of this research hold practical significance for supporting road maintenance operations.
published_date 2025-11-03T05:30:36Z
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score 11.095883

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