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Geomechanics and Engineering Volume 36, Number 1, January10 2024 , pages 1-8 DOI: https://doi.org/10.12989/gae.2024.36.1.001 |
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Void detection for tunnel lining backfill using impact-echo method based on continuous wavelet transform and convolutional neural network |
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Jiyun Lee , Kyuwon Kim, Meiyan Kang, Eun-Soo Hong and Suyoung Choi
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| Abstract | ||
| We propose a new method for detecting voids behind tunnel concrete linings using the impact-echo method that is based on continuous wavelet transform (CWT) and a convolutional neural network (CNN). We first collect experimental data using the impact-echo method and then convert them into time–frequency images via CWT. We provide a CNN model trained using the converted images and experimentally confirm that our proposed model is robust. Moreover, it exhibits outstanding performance in detecting backfill voids and their status. | ||
| Key Words | ||
| continuous wavelet transform; convolutional neural network; impact-echo method; lining backfill; nondestructive testing; void detection | ||
| Address | ||
| Jiyun Lee, Meiyan Kang and Suyoung Choi: Department of Mathematics, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea Kyuwon Kim and Eun-Soo Hong: HBC, Inc., 138, Dunsanjung-ro, Seo-gu, Daejeon, Republic of Korea | ||