# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion  
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1 Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University,  
Chengdu 611756, China;[EMAIL_ADDRESS]  
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Footnote 3: [[https://github.com/tbm/2019/15/](https://github.com/tbm/2019/15/)]([https://github.com/tbm/2019/15/](https://github.com/tbm/2019/15/))  
result in equipment failure and even construction accidents, such as boring instability, collapse and jamming [6; 7]. Consequently, enhancing the efficiency of TBM tunnelling hinges on the ability to accurately assess the current perimeter rock grade of its palm face, a crucial yet challenging aspect that demands attention [8; 9]. The existing methods for predicting the current surrounding rock grade of the tunnel face of TBM tunnels can be categorised into three approaches: theoretical analysis, numerical calculation, and machine learning. In terms of theoretical analysis, [PERSON] and [PERSON] [10] were the first to utilise the limit equilibrium method to construct a set of theoretical analytical models. From these models, they derived the corresponding calculation formulas and applied them to the tunnel face support and its related support mechanisms. [PERSON] et al. [11] also employed the limit equilibrium method to analyse the stability of the tunnel's tunnel face, taking into account the potential for local collapses independent of the conditions of the overburden above the tunnel. [PERSON] [12] proposed a superporous water pressure stability model for analysing the stability of tunnel faces in soft soil excavations. The theoretical analysis method plays an important role in providing a prediction of surrounding rock stability. However, the simplifying assumptions and the uncertainty of model parameter selection still limit its accuracy and universality. In terms of numerical analysis, [PERSON] et al. [13] investigated the damage mechanisms of tunnels in jointed rock bodies, emphasising the importance of understanding the behaviour of rock joints in tunnel stability analysis. [PERSON] et al. [14] proposed an exponential FAI for estimating the stability of brittle enclosing rock bodies based on the analysis of the state of stress and the geometry of the strength envelope. [PERSON] et al. [15] investigated the formation mechanism and changing law of the lateral force of the TBM central cutter, focusing on predicting the average lateral force and analysing the degree of rock fragmentation. Numerical analysis methods have been shown to be capable of accurately simulating complex geological conditions and construction impacts, and providing detailed stress-strain distributions. However, the calculation process is complicated, with a low degree of automation and high sensitivity to parameters. In the domain of machine learning, the rapid accumulation of TBM construction data and the extensive application of artificial intelligence algorithms have led to the emergence of novel approaches and strategies for the prediction research of TBM rock grades [16; 17]. [PERSON] et al. [18] proposed an enhanced support vector regression (SVR) model, which enhances the precision of predicting rock mass parameters by employing the stacked single target technique (SST). [PERSON] et al. [19] proposed an integrated learning model based on Classification and Regression Tree (CART) with the AdaBoost algorithm for predicting the classification of TBM enclosing rocks. [PERSON] et al. [20] proposed a real-time enclosing rock classification prediction method based on big data of TBM operation and an integrated learning stacking technique. The advent of deep learning technology has brought with it a suite of advantages in the processing of complex data and the mining of deep features, thus providing new ideas and methods for the prediction of substandard rocks [21; 22; 23]. [PERSON] et al.