# Soil Classification by Machine Learning Using a Tunnel Boring Machine's Operating Parameters
## 1 Introduction  
Tunnel boring machines (TBMs) are widely used for excavation in diverse environments, from urban to subsea areas, and they offer stable excavation and low noise and vibrations. Optimizing the safety, duration, and expense of TBM tunneling requires the appropriate selection and suitable operation of equipment given considerations of both the ground condition and the site's situation [1]. The optimal operation of equipment is already of great interest to tunnel construction managers and TBM operators, but it is very difficult to reasonably adjust TBM operating parameters during tunneling that do not know the environment ahead of the tunnel face. There is no separate standard that mandates settings in a given situation, and their selection usually depends on the experience of the engineers [2]. The various components and mechanical devices used throughout the TBM tunneling process, from excavation to segment installation, are closely connected, and their operation would benefit greatly from automation. Partial automation has already been achieved, and recent developments of mechanical technology, computing power, and artificial intelligence (AI) are aiding research into the application of machine learning to areas related to the safety and efficiency of constructing tunnels [3]. Several studies used AI techniques to predict settlement and equipment performance. [PERSON] et al. [4] predicted the settlement using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and gene expression programming (GEP) methods. [PERSON] et al. [5] investigated the efficiency and feasibility of six machine learning (ML) algorithms through the field data including geological conditions, shield operational parameters, and tunnel geometry. As a study on TBM performance prediction, [PERSON] et al. [6] applied the support vector regression (SVR) algorithm to predict the penetration rate of TBM based on rock properties and machine parameters in hard rock conditions. In addition, [PERSON] et al. [7] developed a simple artificial neural network (ANN) model for predicting the TBM penetration rate, and as a result of comparison with the hybrid model, it was mentioned that the performance of the hybrid model was superior. [PERSON] et al. [8] predicted TBM operating parameters from real-time measured machine data using RNN-based predictors. They concluded that it shows good performance, in the case of real-time prediction of TBM operating parameters. [PERSON] [9] noted that ground data are limited compared with TBM operating data and assigned the limited ground data as a single engineering soil unit (ESU) when analyzing TBM operating data for each ESU. When applying machine learning, the excavation speed of an earth pressure balance (EPB) shield TBM can be set as a dependent variable (label), and the operating data affecting the excavation speed (e.g., thrust, cutterhead torque, foam flow, and screw conveyor torque) are independent variables (features) that change depending on the condition and type of ground, i.e., the TBM is operated according to the ground it is being used in. A tunnel engineer would agree that it is necessary for the efficiency and safety of the excavation work to predict the ground conditions ahead of the tunnel face. When excavating using TBM, it is effective to utilize machine data of equipment measured in real time and machine learning techniques to predict the ahead ground conditions. Some research showed the possibility of prediction about the ground condition ahead of the tunnel face. The prediction of rock mass parameters is necessary to increase the construction safety and excavation efficiency of TBM. [PERSON] et al. [10], and [PERSON] et al. [11] showed that the ANN model can be used to improve classification efficiency and self-consistency for classifying rock mass behavior types. [PERSON] et al. [12] performed a study to back-calculate the mechanical parameters of rock mass using support vector regression (SVR) optimized by a multi-strategy artificial fish swarm algorithm (MAFSA).