ORIGINAL RESEARCH article
Front. Built Environ.
Sec. Geotechnical Engineering
Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1679410
This article is part of the Research TopicSmart Sensing and Data Analytics in Geotechnical StructuresView all articles
Electromagnetic Wave Response and Intelligent Recognition of Defects at Reconstructed and Expanded Roadbed Junctions
Provisionally accepted
- 1Jiangxi Provincial Key Laboratory of Highway Bridge and Tunnel Engineering & Jiangxi Communications Investment Maintenance Technology Group Co., Ltd, Nanchang, China
- 2Chongqing Jiaotong University, Nan'an District, China
- 3Engineering Research Centre of Diagnosis Technology of Hydro-Construction, Chongqing Jiaotong University, Chongqing, China
- 4Jiangxi Communications Investment Group Co., Nanchang, China
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During the reconstruction and expansion of expressways, defects at the roadbed junction can compromise driving safety and significantly reduce the service life of the road. Based on engineering cases, a generalized model of the defective reconstructed and expanded roadbed junction was developed, and the propagation simulation of electromagnetic waves in the defective roadbed junction was performed using the finite-difference time-domain (FDTD) method. The simulation results demonstrated that the electromagnetic waves formed two sets of parallel convex hyperbolas at the circular cavity defects. The presence of non-compactness defects caused the overall reflected wave signal to exhibit an imaging characteristic with a clear upper section and a blurred lower section. In addition, electromagnetic waves manifested as multiple nearly parallel convex hyperbolas near the vertical cracks. On this basis, by integrating numerical simulation results with field-measured data, a comprehensive dataset encompassing various types of defects was established. Following the optimization of the YOLO algorithm training model, the identification accuracy rates for void, non-compactness, and crack defects reached 97%, 99%, and 99%, respectively. This study provides a new method for highway maintenance, aiming to promote the sustainable development of highway construction. The new method proposed in this study has universal reference value and application potential for road defect detection under different geological conditions and construction standards.
Keywords: Reconstructed and expanded roadbed junction, ground penetrating radar, deep learning, Defect identification, Field testing
Received: 04 Aug 2025; Accepted: 03 Sep 2025.
Copyright: © 2025 Yao, Yang, Gang, Jinzhi, Kui and Chunyan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sun Yang, Jiangxi Provincial Key Laboratory of Highway Bridge and Tunnel Engineering & Jiangxi Communications Investment Maintenance Technology Group Co., Ltd, Nanchang, China
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