# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 1 Introduction  
Since the beginning of the 21 st century, the world has been facing the challenges of water shortages, excessive population growth, energy crises and many other issues. The demand for water conservancy projects, underground transportation, deep-energy exploitation and so on is growing. Therefore, underground engineering has become an important development direction of infrastructure construction in the world, and tunnel construction is the key control point of these major projects [1]. Due to the wide application of the TBM in tunnel construction and the practical needs of construction projects, many experts and scholars at home and abroad have researched and developed many TBM performance prediction models since the 1970s. Forecasting research can be divided into two categories: theoretical research and empirical model research. Most of the theoretical model research is based on laboratory tests and numerical simulations, or semi-theoretical research based on engineering knowledge, and then it carries out the prediction of the TBM performance. [PERSON] [2] thought that the rock breaking of the TBM hob is caused by the tensile failure of rock, and based on this assumption, the performance prediction formula of the TBM hob in the stratified and schistose rock mass is proposed. [PERSON] [3] explored the influence of hob size on the TBM penetration rate. [PERSON] [4] proposed the prediction formula of the TBM penetration rate based on rock mass specific energy, cutterhead power, tunnel cross-sectional area and the machine efficiency factor by using the dimensional analysis method. [PERSON] [5] and [PERSON] [6,7] proposedand improved the famous Colorado school of mines (CSM) model. The model summarized a large number of laws through the full scale linear cutting machine to obtain the hob load and combined this with the specific rock mass parameters and mechanical parameters to obtain the prediction formula of the TBM penetration rate. The theoretical analysis based on laboratory tests and numerical simulation is helpful to further reveal the rock breaking mechanism of the TBM hob. However, TBM construction is a systematic construction method integrating tunnel excavation, design and support, so the research on the TBM construction site is the mainstream research direction of TBM performance prediction. In addition, a variety of empirical models are used to predict the TBM driving speed. Based on the geological data and the TBM performance parameters of eight TBM tunnels, [PERSON] et al. [8] proposed that the rock tensile strength (\(\sigma_{t}\)) and average thrust of cutterhead (\(F_{\text{n}}\)) should be used to calculate the penetration. Based on 112 groups of rock mass parameters and TBM performance parameters of five TBM tunneling projects in Italy, [PERSON] et al. proposed a method to calculate the TBM penetration rate by using the rock structure score (RSR) and uniaxial compressive strength (UCS). [PERSON] and [PERSON] [9] quantitatively analyzed the influence of the uniaxial compressive strength (UCS), rock britteness index (\(B_{\text{i}}\)), number of unit volumetric joints (\(J_{\text{v}}\)) and angle between joint surface and tunnel axis (\(\alpha\)) on penetration based on the relevant mechanical-rock parameters collected during the TBM excavation of the Deep Tunnel Sewerage System (DTSS) Project in Singapore. Combined with the above parameters, the prediction formula of the specific rock mass drivability index was proposed by using a non-linear regression analysis. [PERSON] et al. [10] analyzed the relevant data of TBM tunneling in a granite lithology in Northeast China, and the research showed that the penetration index (FPI) had a strong correlation with uniaxial compressive strength (UCS), rock integrity coefficient (\(K_{\text{v}}\)), and cutterhead thrust (\(F\)) and established a multiple regression relationship. [PERSON] and [PERSON] et al. [11] divided TBM cutterhead rock breaking into three stages: extrusion, initiation and breaking.