## 1 Introduction  
Tunnel boring machines (TBMs) are currently the most utilized equipment for deep and long tunnels in both civil and mining industries. One important consideration prior to the actual excavation is evaluating ground conditions along the proposed tunnel alignment. This initial evaluation provides critical information for selecting the excavation type and developing preliminary ground support systems. Ground conditions are obtained by the characterization and subsequent classification of the rock mass based on a pre-defined system known as a rock mass classification system. Since the introduction of rock mass classification by [PERSON], it has become a useful tool for rock engineering and is widely considered the most practical method for evaluating the quality of the rock mass in underground engineering practices. The common and widely used classification systems are the Q-system [1], Rock Mass Rating (RMR) [2], Rock Mass Index (RMi) [3], and Geological Strength Index (GSI) [4]. Aside from these classification systems, the Japanese HighwayClassification System (JH system) and the Hydropower Classification System (HC system) are also popular in Asia. One of the serious concerns in the use of rock mass classifications schemes is that they are subjective. Field engineers with different experience levels classifying the same rock mass using for example, RMR, can produce significantly different rock mass behavior [5]. This is because most of these classification systems use both quantitative and qualitative methodologies. To reduce, if not to eliminate, the subjectivity or experience factor in rock mass classification, a data-driven system is necessary. Some of the early attempts on data-driven approaches focused on the use of non-destructive forward geological prospecting techniques including tunnel seismic prediction (TSP), and ground penetration radar (GPR) to assess the rock mass quality ahead of TBMs [6; 7]. Although these geophysical techniques provide reliable and accurate results, they are expensive and cause undue project delays. [PERSON] et al. [8] indicated that these forward geophysical prospecting techniques are not directly related to the rock tunneling/excavation process since they can only be implemented when the TBM is not in operation. Besides the subjective nature of rock mass classification systems, limited space between the TBM cutterhead and the tunnel face makes geologic mapping for classifying in-situ ground conditions difficult, if not impossible [9]. Another data-driven approach for classification of rock mass conditions in tunnels excavated by TBMs is the application of artificial intelligence (AI) and machine learning (ML) techniques to TBM operating parameters. Several researchers [10; 11; 12; 13; 14; 15; 16; 17] have applied ML algorithms, capable of handling complex non-linear problems, to establish the relationship between TBM operational data and rock mass conditions. [PERSON] et al. [18] used cutterhead thrust, cutterhead torque, revolution per minute (RPM), and penetration rate to develop a simulated annealing-back propagation neural network (SA-BPNN) model to predict rock mass properties (UCS, brittleness index (Bi), and the distance between plane of weakness (DPW). Current research in rock excavation and tunneling is focused on developing reliable AI and ML models based exclusively on TBM operational data. The overall objective of these efforts is to develop some kind of on-board rock mass classification system on TBMs that will allow automated rock mass classification and possibly ground support system selection. [PERSON] et al. [19] used TBM operational data to train a support vector classifier coupled with genetic algorithm to classify rock masses based on the improved basic quality (BQ) classification system. [PERSON] et al. [20] applied ANN to shield TBM operational data (penetration rate, cutterhead torque and thrust force) to predict ground conditions ahead of the TBM. [PERSON] et al.