机器学习的数据驱动方法为隧道掘进机（tunnel boring machine, TBM）施工智能化赋能，对于优化掘进工艺、提高掘进安全性和降低人工成本至关重要。针对TBM运行数据噪声多、参数冗余及有效特征提取困难的难题，运用数据驱动的机器学习方法，挖掘数据蕴含的机-土复杂相互作用，实现TBM围岩岩体分类预测。首先，对于TBM掘进过程中产生的大量运行数据，采用核密度估计（kernel density estimation, KDE）对典型掘进参数曲线提取特征，获取稳定掘进阶段TBM关键运行参数的最大概率。然后，面向实际场景TBM运行数据，提出围岩分类堆叠集成学习算法，通过k-fold交叉验证进一步优化算法，利用基分类器和元分类器两层学习框架挖掘数据中的复杂关系。最后，采用5 868个TBM掘进段的数据集对该算法的有效性进行验证。结果表明，四分类问题预测的平均F₁达到0.705，二分类问题预测的平均F₁达到0.797，其预测效果均优于所选的四种基分类器。

The data-driven approach of machine learning enables the intelligent construction of TBM (tunnel boring machines), which is crucial for optimizing the tunneling process, improving the safety of tunneling and reducing labor costs. In order to solve the problems of excessive noise, redundant parameters and difficult effective feature extraction in TBM operation data, a data-driven machine learning method was used to mine the complex machine-soil interaction contained in the data and realize the classification and prediction of TBM surrounding rock mass. First, for the large amount of operational data generated during TBM tunneling, the KDE(kernel density estimation) method was used to extract features from typical tunneling parameter curves, and the maximum probability of the key operating parameters during stable tunneling stage of TBM is obtained. Then, based on the actual TBM operation data, an integrated learning algorithm for surrounding rock classification stacking was proposed. The algorithm is further optimized through k-fold cross-validation, and the complex relationships in the data are mined by using the two-layer learning framework of base classifier and meta-classifier. Finally, a data set of 5 868 TBM segments was used to verify the effectiveness of the proposed algorithm. The results show that the average F₁ of the four-classification problem is 0.705, and the average F₁ of the two-classification problem is 0.797, which are better than the four selected base classifiers.