Deep Learning–Based Prediction of Tunnel Face Stability in Layered Soils Using Images of Random Fields
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 150, Issue 8
Abstract
The stability analysis of tunnel faces in multilayered soils presents challenges due to the inherent variability in natural soils. Although the random field finite-element methods offer a reliable approach to address such variability, their heavy computational demands have been a significant drawback. To overcome this limitation, this study presents a novel deep learning–based method for efficient tunnel face stability analysis in layered soils with spatial variability. By combining the merits of convolutional neural networks (CNNs) and U-Net, the proposed method trains surrogate models using a small but sufficient number of random field images to effectively learn high-level features that encompass spatial variabilities, which significantly enhances computational efficiency. In particular, U-Net generates precise displacement field images based on random field images, enabling the discrimination of tunnel face collapse failure modes. To validate the effectiveness of this proposal, a comprehensive case study involving layered soils with spatial variabilities is conducted. The remarkable agreement between the outputs of CNNs and U-Net and the predictions of finite-element simulations underscores the promising potential of using deep-learning models as a surrogate for analyzing the stability of tunnel faces in spatially variable layered soils. Last but not least, the key innovation of this work lies in the pioneering application of U-Net for geotechnical reliability analysis.
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Data Availability Statement
Some or all data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work is supported by the Natural Science Foundation of China (NSFC Grants U22A20594 and 52079045) and the Fundamental Research Funds for the Central Universities (B230205028). Zheming Zhang would like to thank China Scholarship Council for providing a scholarship (Grant 202206710106) in Singapore, and Ze Zhou Wang would like to acknowledge his research funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant agreement 101034337.
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Information & Authors
Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 150 • Issue 8 • August 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jul 15, 2023
Accepted: Feb 21, 2024
Published online: Jun 5, 2024
Published in print: Aug 1, 2024
Discussion open until: Nov 5, 2024
ASCE Technical Topics:
- Engineering fundamentals
- Failure modes
- Finite element method
- Forensic engineering
- Geomechanics
- Geometry
- Geotechnical engineering
- Layered soils
- Mathematics
- Methodology (by type)
- Numerical methods
- Soil analysis
- Soil dynamics
- Soil mechanics
- Soil properties
- Soil stabilization
- Soils (by type)
- Spatial variability
- Structural engineering
- Tunnels
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