## 1 Introduction  
After the excavation of the tunnel, the original stress balance of the rock mass around the roadway was destroyed, which leads to the redistribution of original stress and has an impact on the surrounding underground structures including surrounding rock mass and surrounding filling bodies [1; 2; 3]. Meanwhile, the reduction of surrounding rock mass strength and the stress concentration will appear. The rock mass will be ruptured while the concentrated stress is greater than the surrounding rock mass strength, until the new stress balance created, and a broken rock zone (BRZ) will be developed during that process (see Figure 1). According to the study of [PERSON] and [PERSON] [4] and [PERSON] and [PERSON] [5], the broken rock zone thickness (\(BRZT\)) can be used to describe the broken rock zone (BRZ), analyze the roadway surrounding rock stability and guide the roadway support design. Therefore, a simple and high-precision broken rock zone (BRZ) predictive method is important and meaningful. For the accurate broken rock zone thickness (_BRZT_) determination, three methods including the empirical formula method, the on-site measurement method and the machine learning method were proposed. For the empirical formula method, [PERSON] [6] proposed an empirical formula for the thickness prediction based on the wave velocity of rock mass and rock. [PERSON] [7; 8; 9; 10] proposed the discontinuous zone concept and the empirical formula of the discontinuous zone thickness. [PERSON] [11] proposed a broken rock zone predictive formula based on the indoor test and filed experience. Although these formulas can be used to predict the broken rock zone thickness (_BRZT_), only a few factors are considered, a few engineering sites have been verified, and it is hard to be applied in the complex engineering environment. For the on-site measurement method, the geological radar method, the borehole camera method and the ultrasonic method were considered in the broken rock zone (BRZ) measurement, and were applied in many successful cases, such as Tongsheng Tunnel [12] and Jiaojia Gold Mine [13]. These on-site measurement methods are practical but time- and labor-consuming. For the machine learning method, several studies were conducted by worldwide researchers. For example, [PERSON] and [PERSON] [14] tested the feasibility of using a support vector machine to evaluate the broken rock zone thickness (_BRZT_) based on 132 datasets collected from many coal roadways. [PERSON] and [PERSON] [4] also proposed a support vector machine (SVM) model to determine the broken rock zone thickness (_BRZT_), and the average relative error of that model is 2.66%, which means that the obtained results agree well with the actual _BRZT_ values. [PERSON] [15] combined the PSO algorithm and least square support vector machines (LSSVM), based on a 32 datasets database, and the developed PSO-LSSVM was verified to have the characteristic of fast convergence speed and high calculation accuracy. [PERSON] [16] utilized the artificial neural network (ANN) model to predict the broken rock zone thickness (_BRZT_), and the relative error of 9% can powerfully prove that the developed ANN model has high precision. [PERSON] [17] proposed an RBF neural network model based on Matlab 6.5, and the relative error of two testing datasets is respectively 0.4% and 0.92%. [PERSON] [18] and [PERSON] et al. [19] applied the adaptive neuro-fuzzy network to predict the broken rock zone thickness (_BRZT_) and achieved nice predictive performance. [PERSON] [20] combined the genetic algorithm (GA) and backpropagation (BP) neural network to analyze an 18 datasets database and found that the predictive performance of the new proposed GA-BP is much better than the original BP model. [PERSON] et al. [21] utilized a wavelet relevance vector machine in broken rock zone (BRZ) prediction, and the proposed model is showing a square correlation coefficient of 0.95. In recent years, artificial intelligence (AI) technology has developed very rapidly [22; 23].