# Soil Classification by Machine Learning Using a Tunnel Boring Machine's Operating Parameters
## 1 Introduction
As a study using neural networks, [PERSON] et al. [2] composed the model using a hybrid algorithm (SA-BPNN) and conducted it to compare the SA-BPNN with the existing back propagation neural network (BPNN). The results show the improvement effect of the SA on BPNN has not been verified. Similarly, [PERSON] et al. [13] conducted an experiment to predict the geological conditions using TBM machine data and a generative adversarial network. They proposed a GAN model to estimate the thickness of the rock-soil type on the ground at random locations, which distinguished them from previous studies. [PERSON] et al. [14] carried out a review of AI techniques including machine learning methods used for ground condition prediction ahead of tunnel boring machines. They set the same machine learning model and input parameters used in the literature review and performed learning using data from new sites. The results show that the performance of the model depends on the rock characteristics. Therefore, in order to use an optimized model in a certain ground, it is preferable to use it in a similar ground. In order to apply the machine learning technique, it is first necessary to determine the input parameters. Most researchers selected machine data (penetration rate, thrust force, cutterhead torque, RPM, specific energy, etc.) [15] and ground data (unconfined compression strength, rock quality, designation, Brazilian tensile strength, etc.) to apply techniques such as ANN, SVR, DNN, RF, and XG boost. The decision of input parameters is based on engineering judgement and the findings of existing researchers [1]. [PERSON] et al. [1] conducted a study to predict the penetration rate, which is the main item of TBM's operation, using machine learning techniques. In this study, six input parameterswere selected among the factors derived from the existing literature survey as effective input parameters for predicting the penetration rate. Furthermore, five supervised machine learning techniques (KNN, CHAID, SVM, CART, and NN) were applied to predict the penetration rate. [PERSON] et al. [16] used a clustering method to classify the ground type based on the machine data of TBM. Based on geological parameters such as cohesion and elastic modulus, the ground type was labeled by dividing the ground from hard soil to fair rock mass (the range of RMR 40 to 60) into four clusters, and then a classification model was constructed to present the ground type according to machine data. This study predicted soil classification by using AI to explore relationships between traditional soil classification measurements and EPB TBM operating data, which implicitly depend on the engineering characteristics of the ground. Unlike studies that classify rock based on existing machine data, this study was conducted to classify the ground composed of soil from which rock was excluded. Machine learning techniques available in TBM tunneling research are very diverse. Suitable algorithms are selected according to the characteristics of the data, and it is necessary to analyze the dataset before applying machine learning techniques. In this study, the boosting series, which is an ensemble model of ML, was mainly reviewed among various ML techniques, and the decision tree and the SVM model were compared. As mentioned above, in order to safely and effectively perform TBM construction, it is necessary to predict the ground condition in front of the excavation face of TBM. In particular, in the case of soft ground, depending on whether it is sandy soil or viscous soil, the builder may vary in preparation for related work, such as the use of form. Considering this, it is essential to classify the characteristics of the soil ahead of the tunnel face. This paper introduces traditional classification methods for classifying soil in Section 2 and describes the classification characteristics (particle size, N-value) of soil to be used in this study, prior to performing the learning.