# Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate
## 1 Introduction  
The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is a longstanding research area. Suitable prediction of TBM performance parameters, notably the penetration rate (PR) and the advance rate (AR), can reduce the risks related to high capital costs, which are very common for mechanized excavation operations. This is an essential task for planning tunnel projects and selecting suitable construction methods. During the past decades, several empirical techniques have been developed for forecasting the performance parameters of TBM: PR, and AR [1; 2; 3; 4; 5; 6; 7]. In the aforementioned models/classifications, the most influential factors of rock mass and material, such as compressive and tensile strengths and joint conditions, as well as of machine specifications, such as average cutter force, and cutter-head torque, were utilized as predictors. A PR model was introduced by [PERSON] and [PERSON] [8] based on the tensile strength of the rock and average cutter force. In another study, conducted by [PERSON] [3], specific energy was predicted using values of uniaxial compressive strength (UCS) of the rock. [PERSON] [9] developed the Colorado School of Mines (CSM) model based on a database comprising of cutter and rock properties to estimate the cutter forces. The CSM model has since been modified several times by [PERSON] [5], [PERSON] and [PERSON] [10], and [PERSON] [11], by adding the effects of rock mass brittleness and rock mass fracture properties. Although these models have several inputs, most of these studies rely on rock properties, such as strength, joint spacing, and brittleness, as inputs into the established techniques [12; 13; 14; 15; 16]. Several studies used traditional statistical and linear techniques to predict TBM performance [17; 18]. However, these techniques have been criticized, because of their disadvantages in solving highly complex and nonlinear problems. [PERSON] et al. [19] mentioned that statistical models are not always robust enough to accurately describe nonlinear and complex systems. Moreover, their performance capacity is diminished in the presence of outliers and extreme values in the data. Furthermore, [PERSON] et al. [20] stated that multiple parameters' models use more project-specific data (compared to simple models), and are more easily applied (compared to computer-aided models). It should be mentioned that the performance capacities of the multiple parameters models are higher than those of simple parameter models. This is due to the use of more related input parameters. Hence, it seems that artificial intelligence (AI) and machine learning (ML) techniques can be the best choice to be applied for prediction of TBM performance using multiple input parameters. AI and ML techniques (Table 1), such as artificial neural network (ANN), support vector machine (SVM), particle swarm optimization (PSO), and fuzzy inference system (FIS) have been widely used for solving problems in civil engineering and, more specifically, for TBM performance parameters [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. ANN models were developed by [PERSON] and [PERSON] [36] and [PERSON] et al. [17] to predict TBM PR. Hybrid ANN models of PSO-ANN and imperialist competitive algorithm (ICA)-ANN were developed to approximate PR and AR of TBMs in the study conducted by [PERSON] et al. [12] and [37], respectively. Recently, [PERSON] et al. [13] developed a new model to predict the tunnel boring machine performance based on the group method of data handling. A gene expression programming (GEP) was introduced by [PERSON] et al. [38] for estimating PR values obtained from a transfer tunnel in Malaysia.