Intelligent identification method for pipeline leakage based on GPR time–frequency features and deep learning
Author:
Affiliation:
1. a College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China
2. b Innovation Center of Yangtze River Delta, Zhejiang University, Hangzhou, China
3. c Balance Architecture, Zhejiang University, Hangzhou, China
Abstract
Funder
National Key Research and Development Program
ZJU-ZCCC Institute of Collaborative Innovation
Publisher
IWA Publishing
Link
https://iwaponline.com/aqua/article-pdf/73/7/1421/1460798/jws0731421.pdf
Reference40 articles.
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3. Effect of moisture content on calculated dielectric properties of asphalt concrete pavements from ground-penetrating radar measurements;Remote Sensing,2022
4. Leak detection in water supply network using a data-driven improved graph convolutional network;IEEE Access,2023
5. Field validation of water-pipe leakage detection through spatial and time-lapse analysis of GPR wave velocity;Near Surface Geophysics,2019
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