Abstract: More than 6 million sets of relevant data are collected from a high-altitude railway tunnel to examine the correlations among tunnel boring machine (TBM) tunneling parameters as well as between tunneling parameters and surrounding rock grades. The distribution characteristics and patterns of tunneling parameters for different rock grades are examined via quartile analysis, and a table for optimizing these parameters is proposed. Furthermore, 24 ring-based parameters are extracted from the tunneling data and analyzed using the synthetic minority over-sampling technique to address data imbalance issues. Four machine learning algorithms—particle swarm optimization (PSO)-support vector machine (SVM), genetic algorithm (GA)-SVM, SVM, and Transformer—are employed for surrounding rock grade inversion, and their prediction accuracies are compared. The results indicate that: (1) during TBM tunneling, cutterhead torque exhibits the highest correlation with tunneling speed, with a correlation coefficient of 0.92, and no significant interrelations with other tunneling parameters; the mean value of individual parameters shows limited correlation with rock grades; (2) TBM tunneling parameters for surrounding rocks of Grades Ⅱ and Ⅲ are more concentrated and fluctuate less than those for rocks of Grades Ⅳ and Ⅴ; with increasing rock grade, tunneling speed generally increases, cutterhead torque decreases, and cutterhead rotation speed and total thrust initially decrease and then increase; and (3) the PSO-SVM model is more accurate in predicting the rock grade compared to the SVM, GA-SVM, and Transformer models, exhibiting a reasonably distributed confusion matrix. Its overall accuracy reaches 88.6%, whereas individual prediction accuracies for surrounding rocks of Grades Ⅱ,Ⅲ,Ⅳ, and Ⅴ are 85.0%, 93.1%, 82.8%, and 71.4%, respectively.

Key words: TBM, machine learning, surrounding rock grade, tunneling parameters, particle swarm optimization-support vector machine model