Subsurface Object 3D Modeling Based on Ground Penetration Radar Using Deep Neural Network-Reference-Cited by-同舟云学术

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Subsurface Object 3D Modeling Based on Ground Penetration Radar Using Deep Neural Network

Published:2023-11 Issue:6 Volume:37 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Feng Jinglun¹,Yang Liang²,Xiao Jizhong³

Affiliation:

1. Ph.D. Candidate, Dept. of Electrical and Engineering, The City College of New York, 160 Convent Ave., New York, NY 10031.

2. Research Assistant, Dept. of Electrical and Engineering, The City College of New York, 160 Convent Ave., New York, NY 10031.

3. Professor, Dept. of Electrical and Engineering, The City College of New York, 160 Convent Ave., New York, NY 10031 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5359

Reference68 articles.

1. Automatic detection of hyperbolic signatures in ground-penetrating radar data

2. Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition

3. Water content evaluation in unsaturated soil using GPR signal analysis in the frequency domain

4. Remote Sensing of Soil Moisture Content by GPR Signal Processing in the Frequency Domain

5. Chen H. and A. G. Cohn. 2010. “Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data.” In Proc. 2010 Int. Joint Conf. on Neural Networks (IJCNN) 1–8. New York: IEEE.

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1. Variable data structures and customized deep learning surrogates for computationally efficient and reliable characterization of buried objects;Scientific Reports;2024-06-28

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