Current assessments of rock mass quality of a NATM tunnel face are important in the practice of tunnel excavation. This study establishes a multi-source database and proposes a data driven method for the assessment. Thirteen multi-source variables describing the tunnel faces are considered as inputs, and the rock mass rating (RMR) values computed by the empirical formula are the target outputs. We adopted two meta machine learning models (classification and regression tree (CART) and multiple layers perceptron (MLP)) and two ensemble learning models (gradient boosting regression tree (GBRT)) and random forest (RF)) to capture the relationships between the inputs and outputs. The tree-structured Parzen estimator (TPE) algorithm is applied to automatically determine the optimized model hyper-parameters. The experimental results suggest that the proposed hybrid ensemble learning models (TPE-RF and TPE-GBRT) perform well at assessing rock mass quality. The feature importance ranks of the input variables are determined by a sensitivity analysis, which enhances the knowledge on assessing the rock mass quality of a tunnel face.

目前对NATM隧道掌子面岩体质量的评估在隧道开挖实践中很重要。本研究建立了一个多源数据库，并提出了一种数据驱动的评估方法。将描述隧道面的 13 个多源变量视为输入，通过经验公式计算的岩体评级 (RMR) 值是目标输出。我们采用了两个元机器学习模型（分类和回归树（CART）和多层感知器（MLP））和两个集成学习模型（梯度提升回归树（GBRT））和随机森林（RF））来捕捉输入和输出之间的关系。树结构的 Parzen 估计器 (TPE) 算法用于自动确定优化的模型超参数。实验结果表明，所提出的混合集成学习模型（TPE-RF 和 TPE-GBRT）在评估岩体质量方面表现良好。输入变量的特征重要性等级由敏感性分析确定，这增强了评估隧道掌子面岩体质量的知识。