Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading: A comprehensive FELA analysis using three stability factors and machine learning
This study examines the stability of three-dimensional rectangular tunnel headings in drained c-ϕ soils, incorporating surcharge effects using 3D Finite Element Limit Analysis (FELA). It focuses on the upper and lower bound solutions for three stability factors: cohesion, surcharge, and soil unit we...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2025-06-01
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| Series: | Artificial Intelligence in Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544125000073 |
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| Summary: | This study examines the stability of three-dimensional rectangular tunnel headings in drained c-ϕ soils, incorporating surcharge effects using 3D Finite Element Limit Analysis (FELA). It focuses on the upper and lower bound solutions for three stability factors: cohesion, surcharge, and soil unit weight (Nc, Ns, and Nγ). Based on Terzaghi's principle of superposition, the analysis evaluates tunnel stability under varying parameters, such as cover-depth ratio (H/D), width-depth ratio (B/D), and friction angle (ϕ). The results align closely with previous studies, and practical design charts are provided for calculating minimum support pressures. Additionally, machine learning models (ANN and XGBoost) are used to develop accurate correlations between input parameters and stability results. A relative importance index analysis is conducted to assess the impact of these parameters. This research enhances understanding of tunnel stability and offers practical insights for tunnel design. |
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| ISSN: | 2666-5441 |