###### Abstract
Learning and training samples of the back-propagation neural network were established by numerical software, and then, inverse analyses of the roadbed response coefficient K were carried out using the neural network [18]. A regression model was used to predict tunnel-induced ground settlement [19]. Deep learning can process a large amount of time-series data quickly and has been used by many scholars in recent years to deal with the problem of sedimentation ephemeral variation. By establishing a combined prediction model based on the S-shaped growth curve, it was found that the model not only reflected the change process of settlement but also predicted the surface settlement more accurately [20]. By inputting geological conditions, construction parameters, construction sequence, grouting volume, and grouting time, the ground settlement caused by shield tunnel construction was predicted by using an optimized neural network based on a differential algorithm [21]. The ground settlement caused by tunnel excavation was predicted by analyzing the relationship between ground settlement and shield tunneling parameters, and the analysis results were used as inputs to the BP neural network training model [22]. Based on the measured data, the surface settlement caused by tunnel excavation in karst areas was predicted using an extended one-dimensional convolutional neural network model [23]. In order to provide the reader with an intuitive view of the existing research, the authors have classified the relevant literature in Table 1.  
For long time-series data, the traditional back-propagation neural network model is simple in structure and unable to capture the changing features of the series, and the \begin{table}
\begin{tabular}{c c} \hline
**Classification** & **Specific Methods** \\ \hline Traditional methods & Numerical modeling \\  & Theoretical equations \\  & Grey models \\  & Neural networks \\  & Regression models \\  & Curve-fitting methods \\ \hline \end{tabular}
\end{table} 
Table 1: Classification of available research methods. recurrent neural network suffers from gradient vanishing; however, the long- and short-term memory neural network better ameliorates these problems by setting up the control unit. The LSTM (Long Short-Term Memory) neural network was widely used for various tasks, such as speech recognition, sound modeling, and correlation analysis [24; 25; 26; 27]. Of course, many scholars in different fields have utilized this neural network to predict long time-series data. For example, in the field of civil engineering, some scholars predicted rock shear strength parameters (cohesion and internal friction angle) by constructing a long short-term memory neural network [28]. Some scholars used bidirectional stacked long short-term memory neural networks to predict soil movement [29]. Some scholars predicted hydrological data and drilling logs based on improved LSTM models [30; 31]. In the field of economics, some scholars used LSTM neural networks to predict stock returns and stock price movements [32; 33]. Some scholars have quantitative stock selection strategies based on CNN (convolutional neural networks)-LSTM neural networks [34]. Currently, most of these research studies use neural networks to map the relationship between static parameters such as soil properties and surface settlement [35], ignoring the fact that deep foundation construction causes surface settlement as well as excessive building settlement. Moreover, building settlement may be affected by time-dependent dynamic factors such as construction conditions. For a subway station deep foundation pit project, the construction conditions are complex, the construction period is long, and the time for different construction conditions is different. For example, there is a big difference between the time period of enclosure construction and reinforcement construction, which often has different impacts on the settlement of the building. At the same time, the model prediction accuracy needs to be analyzed.