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

IF 4.2 Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-03-14 DOI:10.1016/j.aiig.2025.100111
Suraparb Keawsawasvong , Jim Shiau , Nhat Tan Duong , Thanachon Promwichai , Rungkhun Banyong , Van Qui Lai
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Abstract

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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加强对具有附加荷载的 c-φ 土层中三维矩形隧道顶稳定性的理解:利用三个稳定因子和机器学习进行综合 FELA 分析
本研究考察了排水c- φ土壤中三维矩形隧道掘进的稳定性,采用三维有限元极限分析(FELA)结合附加效应。它侧重于三个稳定因素的上界和下界解:黏聚力、附加物和土壤单位重量(Nc、Ns和n - γ)。基于Terzaghi的叠加原理,该分析评估了不同参数下的隧道稳定性,如覆盖深度比(H/D)、宽深比(B/D)和摩擦角(ϕ)。结果与前人的研究结果一致,并提供了计算最小支撑压力的实用设计图表。此外,机器学习模型(ANN和XGBoost)用于在输入参数和稳定性结果之间建立准确的相关性。通过相对重要性指数分析来评估这些参数的影响。该研究提高了对隧道稳定性的认识,为隧道设计提供了实用的见解。
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