Surface settlement due to tunnel excavation is affected by several factors. However, no explicit mapping correlation exists between surface settlement and the main influencing factors. In this study, three tree-based methodologies, including classification and regression tree (CART), random forest (RF), and gradient boosting decision tree (GBRT), were implemented to predict the tunneling-induced surface settlement of the South Hong-Mei Road tunnel in Shanghai, where a large mix-shield was used. Thirteen influencing factors within three categories (tunnel geometry, geological conditions, and shield operation factors) were employed as input variables. Results show that the ensemble methods (RF and GBDT) provide superior performance over the single-tree model (CART). Moreover, GBDT has the highest level of prediction accuracy among the three statistical learning methods. The importance of influencing factors on the tunneling-induced surface settlement was probed. The tunnel geometry had the greatest effect on surface settlement. It is followed by the influencing factors in shield operation factors. Moreover, geological conditions were not as influential as the other influencing factors. The outcomes of this study may provide a reference for evaluating tunneling-induced surface settlement in other similar tunnel projects.

隧道开挖引起的地表沉降受多种因素影响。然而，地表沉降与主要影响因素之间不存在明确的制图相关性。在这项研究中，采用了三种基于树的方法，包括分类回归树（CART）、随机森林（RF）和梯度提升决策树（GBRT）来预测南红梅隧道开挖引起的地表沉降。上海公路隧道，使用了大型混合盾构。采用三类（隧道几何形状、地质条件和盾构作业因素）内的 13 个影响因素作为输入变量。结果表明，集成方法（RF 和 GBDT）提供了优于单树模型 (CART) 的性能。而且，GBDT在三种统计学习方法中具有最高水平的预测精度。探讨了影响因素对隧道施工引起的地表沉降的重要性。隧道几何形状对地表沉降的影响最大。其次是盾构运行因素中的影响因素。此外，地质条件的影响不如其他影响因素。研究结果可为其他类似隧道工程中隧道施工引起的地表沉降评估提供参考。