Road subsurface distress recognition method using multiattribute feature fusion with ground penetrating radar-Reference-Cited by-同舟云学术

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Road subsurface distress recognition method using multiattribute feature fusion with ground penetrating radar

Published:2022-02-16 Issue: Volume: Page:1-13
ISSN:1029-8436
Container-title:International Journal of Pavement Engineering
language:en
Short-container-title:International Journal of Pavement Engineering

Author:

Yue Guanghua¹²,Du Yuchuan¹^ORCID,Liu Chenglong¹³^ORCID,Guo Shili⁴,Li Yishun¹,Gao Qian¹

Affiliation:

1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, People’s Republic of China

2. Henan Wanli Transportation Technology Group Co.,Ltd, Henan, People’s Republic of China

3. Shanghai Engineering Research Center of Urban Infrastructure Renewal, Shanghai, People’s Republic of China

4. College of Resource and Environment, Henan Institute of Engineering, Henan, People’s Republic of China

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2022.2037591

Reference41 articles.

1. Characterization of surface crack depth and repair evaluation using Rayleigh waves

2. Data Analysis Techniques for GPR Used for Assessing Railroad Ballast in High Radio-Frequency Environment

3. Use of FWD Data for Prediction of Bonding Between Pavement Layers

4. An overview of ground-penetrating radar signal processing techniques for road inspections

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection;Applied Sciences;2024-03-24

2. Advances in automatic identification of road subsurface distress using ground penetrating radar: State of the art and future trends;Automation in Construction;2024-02

3. Bayesian optimization based random forest and extreme gradient boosting for the pavement density prediction in GPR detection;Construction and Building Materials;2023-07

4. GPR Data Augmentation Methods by Incorporating Domain Knowledge;Applied Sciences;2022-10-27

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