## 4 Understanding of TBM Performance  
### Selection of Excavation Methods  
Rock collapse during deep rock excavation can be influenced by the release of strain energy in the surrounding rocks. Several factors can trigger the release of strain energy in tunnels, with the choice of excavation method being of particular significance. Presently, there are two primary methods for tunnel excavation, the TBM method and the drill and blast method. Each of these methods has distinct impacts on the surrounding rock, leading to varying types of damage and necessitating unique reinforcement measures. The advantages and disadvantages of these excavation methods are summarized in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **First Author** & **Time** & **Title** & **Journal** & **Cited** \\ \hline
1 & [PERSON] & & Development of hybrid intelligent & & **Frequency** \\ \hline
1 & [PERSON];[PERSON], & 2017 & 2017 &
\begin{tabular}{c} Development of hybrid intelligent \\ models for predicting TBM \\ penetration rate in hard \\ rock condition \\ \end{tabular} & TUNNELLING AND & & \\
2 & [PERSON], [PERSON] & 2019 &
\begin{tabular}{c} Development of hybrid intelligent \\ models for predicting TBM \\ penetration rate in hard \\ rock condition \\ \end{tabular} & TUNNELLING AND & & \\
2 & [PERSON], [PERSON] & 2019 &
\begin{tabular}{c} UnNDERGROUND SPACE \\ TECHNOLOGY \\ \end{tabular} & 218 & \\
3 & [PERSON], [PERSON];[PERSON], [PERSON] & 2019 &
\begin{tabular}{c} Application of several optimization \\ techniques for estimating TBM \\ advance rate in granitic rocks \\ \end{tabular} & JOURNAAL OF ROCK & \\
3 & [PERSON], [PERSON];[PERSON], [PERSON] & 2019 &
\begin{tabular}{c} Application of several optimization \\ techniques for estimating TBM \\ advance rate in granitic rocks \\ \end{tabular} & JOURNAAL OF ROCK & \\
4 & [PERSON], [PERSON]; [PERSON], & 2021 &
\begin{tabular}{c} Application of several optimization \\ techniques for estimating TBM \\ advance rate in granitic rocks \\ \end{tabular} & MECHANICS AND & & \\
4 & [PERSON], [PERSON]; [PERSON], & 2021 &
\begin{tabular}{c} Unchainerking APPLICATIONS \\ OF ARTIFICIAL INTELLIGENCE \\ \end{tabular} & 86 & \\
4 & [PERSON]; [PERSON], & 2021 &
\begin{tabular}{c} Forecasting TBM advance rate \\ Predicting tunnel boring machine \\ performance through a new model \\ based on the group method of \\ data handling \\ Development of a new hybrid ANN \\ for solving a geotechnical problem \\ related to tunnel boring \\ machine performance \\ \end{tabular} & BULLETIN OF ENGINEERING & \\
5 & [PERSON], J & 2021 &
\begin{tabular}{c} GeOLOGY AND THE \\ ENVIRONMENT \\ \end{tabular} & 84 & \\
6 & [PERSON], [PERSON];[PERSON], & 2020 &
\begin{tabular}{c} Development of a new hybrid ANN \\ for solving a geotechnical problem \\ related to tunnel boring \\ machine performance \\ \end{tabular} & ENGINEERING WITH & 80 \\
7 & [PERSON], [PERSON]; [PERSON], & 2021 &
\begin{tabular}{c} Predicting TBM penetration rate in \\ hard rock condition: A comparative \\ study among six XGB-based \\ metaheuristic techniques \\ \end{tabular} & GEOSCIENCE FRONTIERS & 71 \\
8 & [PERSON], [PERSON];[PERSON], & 2020 &
\begin{tabular}{c} Affect of Water Content on \\ Argillization of Mudstone During \\ the Tunnelling process \\ Estimation of the TBM advance rate \\ under hard rock conditions using \\ XGBoost and Bayesian optimization \\ Prediction of Disc Cutter Life \\ During Shield Tunneling with AI \\ via the Incorporation of a Genetic \\ Algorithm into a GMDH-Type \\ Neural Network \\ \end{tabular} & ROCK MECHANICS AND & \\
10 & [PERSON], [PERSON]; [PERSON], [PERSON] & 2020 &
\begin{tabular}{c} ProCK ENGINEERING \\ \end{tabular} & 49 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Key references in TBM performance studies. In recent years, numerous scholars have conducted extensive research on the extent of surrounding rock damage resulting from different excavation methods. For instance, [PERSON] et al.