覆土盾构隧道的土壤抗浮力对其防浮稳定性有着重要影响。然而，目前关于异形盾构隧道的抗浮研究还相对有限。本研究采用数值模拟结合机器学习研究异性盾构隧道的抗浮特性。本文总结了异性盾构隧道的几何形态，引入形状系数，通过Plaxis3D有限元软件，开展六个关键参数的模拟——形状系数、埋深比、隧道最长水平长度、内摩擦角、黏聚力和土壤浸没体积密度。研究这六个参数在不同条件下对抗浮力的影响。采用XGBoost和ANN机器学习方法，基于数值模拟结果分析了每个参数的特征重要性。研究发现，覆土的抗浮力随隧道形状越接近圆形而降低，而其他参数呈现出相反的趋势。此外本文还揭示了埋深比、内摩擦角、隧道最长水平长度、黏聚力、土壤浸没体积密度和形状系数在影响抗浮力方面的特征重要性呈现递减趋势。

The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability. However, the research on uplift resistance concerning special-shaped shield tunnels is limited. This study utilizes numerical simulation and machine learning techniques to explore this research field. It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient. Through Plaxis3D finite element software, the study simulates six key parameters—shape coefficient, burial depth ratio, tunnel"s longest horizontal length, internal friction angle, cohesion, and soil submerged bulk density—that impact uplift resistance across different conditions. Employing XGBoost and ANN machine learning methods, the feature importance of each parameter was analyzed based on the numerical simulation results. The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil, whereas other parameters exhibit contrary effects. Furthermore, the study reveals a diminishing trend in the feature importance of buried depth ratio, internal friction angle, tunnel longest horizontal length, cohesion, soil submerged bulk density, and shape coefficient in influencing uplift resistance.

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