Width estimation of hidden cracks in tunnel lining based on time-frequency analysis of GPR data and back propagation neural network optimized by genetic algorithm-Reference-Cited by-同舟云学术

Width estimation of hidden cracks in tunnel lining based on time-frequency analysis of GPR data and back propagation neural network optimized by genetic algorithm

Published:2024-06 Issue: Volume:162 Page:105394
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Hou Lili,Zhang Qian,Du Yanliang

Funder

Hebei Province Science and Technology Support Program

CSC

NSAF Joint Fund

Hebei Provincial Natural Science Foundation

Publisher

Elsevier BV

Reference39 articles.

1. J. Fu, J. Xie, S. Wang, J. Yang, F. Yang, H. Pu. Cracking performance of an operational tunnel lining due to local construction defects. Int. J. Geomech., 19 (4) (2019) 04019019. doi: 10.1016/ j.tws.2022.110117.

2. Past, present and future of robotic tunnel inspection;Montero;Autom. Constr.,2015

3. H. Liu, Y. Yue, S. Lai, X. Meng, Y. Du, J. Cui, F. Spencer. Evaluation of the antenna parameters for inspection of hidden defects behind a reinforced shield tunnel using GPR. Tunn. Undergr. Space Technol., 140 (2023) 105265. doi: 10.1016/ j.tust.2023.105265.

4. B. Chiaia, G. Marasco, S. Aiello. Deep convolutional neural network for multi-level non-invasive tunnel lining assessment. Front. Struct. Civ. Eng., 16 (2) (2022) pp.214–223. doi: 10.1007/ s11709–021–0800-2.

5. Volumetric water content estimation of concrete by particle swarm optimization of GPR data;Kaplanvural;Constr. Build. Mater.,2023

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