Abstract:  The relationship between tunnel boring machine (TBM) tunneling parameters and surrounding rock grades is necessary to attain dynamic adjustment of rock machine information mutual feedback perception as well as optimization and adjustment of TBM tunneling parameters. As a result, data cleaning, distribution statistics, and intelligent prediction are used to establish a prediction and identification method of surrounding rock grades based on self-organizing maps and least squares support vector machine (SVM). The following are the main conclusions reached: (1) A single complete TBM excavation cycle can be divided into section bored without force, rising section, and stable section, and each tunneling parameter approximately obeys the normal distribution relationship. (2) The field penetration index (FPI) and cutterhead torque penetration index (TPI) can indicate the difficulty of tunnel rock excavation. The FPT and TPI are approximately linear to the grade of surrounding rock, which can be used as the sensitive factors of rock machines to inversely predict and identify the grade of surrounding rock. (3) The preprocessing of sample points of interference anomaly data affects the convergence center and fluctuation radius of the surrounding rock prediction model. The ability to accurately predict and identify surrounding rock grades depends on data preprocessing. (4) Different SVM kernel functions have a large impact on surrounding rock grade prediction and identification according to standard test data samples and engineering data. The comprehensive identification rates of surrounding rocks using linear, polynomial, and Gaussian radial basis function kernels are 70.8%, 81.2%, and 87.6%, respectively. The surrounding rock grade prediction and identification model has high predictive accuracy and robustness.

Key words: tunnel boring machine, self-organizing maps, support vector machine, tunneling parameters, data mining, inversion prediction and identification, surrounding rock grade