## 2 Literature Review  
Analytical, empirical, and numerical methods have been widely used to estimate the amount of settlement caused by tunnel excavations. [PERSON] [2] proposed a very well-known empirical solution using an inverted Gaussian distribution curve, as shown in Figure 1. Several variations of the empirical solution have been applied [6; 7]. Although empirical solutions are easy to apply, they can only accommodate a handful of factors out of the various factors shown in Figure 2, and often exclude various temporally and spatially relevant factors, such as localized geological structures and an advance rate [8; 9]. Analytical solutions based on soil and rock mechanics, which are also easy to apply and enjoy a comparable academic foundation, also suffer from similar limitations. Numerical methods offer advantages over other empirical or analytical methods to build models that accommodate the constitutive equations, geological layers, localized geological structures, and construction sequences. However, they require the elaboration of time-consuming models in addition to the uncertainty of geotechnical properties [10]. To tackle these issues and to resolve the non-linear relationship between ground reactions and subsidence, the various AI models and machine-learning (ML) approaches have been applied using data from the sensors of a TBM [5; 11; 12; 13] as summarized in Table 1.  
Figure 1: A traverse settlement trough ([PERSON], 1969). Machine-learning algorithms have become increasingly popular with the advent of deep-learning (DL) algorithms. Many attempts to apply ML to geotechnical problems were made even before the advent of DL. Examples include artificial neural networks (ANNs) [14; 15; 18], adaptive neuro fuzzy-based inference (ANFIS) [12], decision trees (DT) [19], back-propagation neural networks (BPNNs) [15; 20; 21], support vector minimization (SVM) [17], and gated recurrent units (GRU) [22]. To estimate subsidence, [PERSON] and [PERSON] adopted a hybrid approach using an ANN model reinforced by a differential evolutionary algorithm [17]. Similarly, [PERSON] and [PERSON] adopted ANN for the assessment of risks of abandoned mine subsidence [23]. [PERSON] et al. and [PERSON] et al. applied various ML and DL algorithms, including a BPNN, wavelet neural network (WNN), logistic regression, extreme learning machine (ELM), SVM, and random forests (RF) to estimate the maximum subsidence due to tunnel excavation, however, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**AI Model** & **Researcher** & **Theme** & **Year** \\ \hline PCA/ANFIS & [PERSON]; [PERSON] [12] & Ground surface settlements induced by shield tunneling & 2017 \\ \hline Evolutionary Hybrid & [PERSON]; [PERSON]; [PERSON]; & Predicting shield tunneling induced ground settlement & 2020 \\ Machine Learning & [PERSON]; [PERSON]; [PERSON]; [PERSON]; [13] & Predicting shield tunneling induced ground settlement & 2019 \\ \hline ANN (Artificial Neural Network) & [PERSON]; [PERSON]; [PERSON]; [PERSON]; & Prediction of ground surface settlement due to tunneling & 2001 \\ \cline{2-4}  & [PERSON];[PERSON] [15] & Prediction the max. surface settlement caused by EPB Shield & 2006 \\ \hline PSO-ANN & [PERSON]; [PERSON]; [PERSON]; & Predicting surface settlement caused by tunneling & 2016 \\ \hline SVM (Support Vector & [PERSON] [17] & Settlement of shallow foundation on cohesionless soil & 2008 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: AI models for predicting surface settlement induced by tunneling. Figure 2: Five phases of settlement according to TBM construction. the estimation of maximum settlement with AI is hardly more valuable than analytical, empirical or numerical methods [13]. Some of the common problems of small datasets are over-fitting and the poor performance of models when it comes to applying them to real world problems, as the model is trained on a subset of real-world conditions.