针对采用机器学习方法预测盾构掘进地表沉降的研究，围绕预测模型的输入参数、预测目标、算法选取和超参数智能优化4个方面的研究进展开展系统综述，总结出当前研究中亟需解决的关键问题，并展望该领域的未来发展方向。研究表明： 1)结合隧道几何参数、地层参数和盾构操作参数等信息进行沉降预测是当前主流的研究方向； 2)沉降预测前需根据预测目标选取合适的模型和输入参数； 3)通过超参数智能算法优化模型参数以提升预测精度。然而，现阶段的研究仍面临着诸多挑战： 1)预测模型普遍缺乏特征自主识别能力且易发生过拟合； 2)对海量数据的挖掘与分析尚不深入； 3)尚未构建基于多源异构数据集的强鲁棒性模型； 4)对地表沉降发展过程的预测研究相对匮乏。最后，展望盾构隧道智能掘进领域中需重点攻克的难题。

The authors systematically review the progress of machine learning applications in predicting surface settlement caused by shield tunneling, focusing on input parameters, prediction objectives, algorithms selection, and hyperparameter optimization. Key challenges are identified, and future research directions are proposed. The findings include the following: (1) Integration of tunnel geometric parameters, stratum properties, and shield machine operation parameters constitutes the predominant research focus for settlement prediction. (2) Selecting suitable models and input parameters tailored to specific prediction objectives is critical. (3) Intelligent hyperparameter optimization can significantly enhance prediction accuracy. However, current studies face several limitations: (1) Most models lack the ability to autonomously identify relevant features and are susceptible to overfitting; (2) Analysis and utilization of large-scale datasets remain inadequate; (3) Robust models leveraging multi-source heterogeneous datasets are yet to be developed; and (4) Research on predicting the developmental processes of surface settlement is relatively scarce. Finally, critical issues requiring attention in advancing intelligent shield tunneling are discussed.