## 5 Conclusions  
Accurate prediction of building settlement near deep foundation pit excavation is a prerequisite for the safe construction of deep foundation pit projects. This paper takes a  
Figure 10: Comparison of single-point predicted and multi-point co-predicted and monitored values.  
deep foundation pit project of a metro station in Shanghai as an example, establishes the Long Short-Term Memory (LSTM) settlement prediction model using building settlement monitoring data, and obtains the following conclusions.  
1. Hyperparameters have a key role in the predictive ability of network models. This study explores the effects of batch size and training set ratio on model prediction accuracy and finds that when the batch size and training set ratio are taken as 16 and 60%, respectively, the average values of the network evaluation index RMSE are 0.59 and 0.50, respectively, and the RMSE is smaller, at which time the model established has the best prediction effect.
2. When the settlement of the buildings around the foundation pit constructed by the full reversal method is selected for prediction, the prediction accuracy of the network model reaches 14.88%, which is about 3% higher than that of the open excavation method, indicating that the prediction value of the long- and short-term memory neural network model is more accurate when the settlement of the buildings around the foundation pit constructed by the full reversal method is used.
3. The LSTM neural network model established in this paper for predicting the settlement of adjacent buildings caused by deep foundation pit construction has a model accuracy of more than 82%, and the model prediction accuracy is related to the construction parameters (construction conditions), engineering geological parameters (soil gravity, cohesion, friction angle, Poisson's ratio, and pore space ratio), and the more carefully the parameters are considered, the more accurately the model prediction is formulated. The evaluation metrics MAE and \(R^{2}\) were improved by 10.96% percent and 30%, respectively, compared with the use of an Artificial Neural Network (ANN).
4. The accuracy of the network model for predicting single-point settlement using multi-point settlement of multiple buildings collaboratively is 88%. However, the accuracy of the network model predicting single-point settlement using single-point settlement is only 80%, which is about 8% lower than the accuracy of the collaborative prediction using multiple monitoring points.
5. This study investigates the feasibility of using a data-driven model (LSTM neural network) to predict the settlement of surrounding buildings caused by deep foundation pit excavation, and the results of the study can provide a certain basis for the construction of foundation pit projects. The LSTM neural network has the capability of predicting the settlement of surrounding buildings caused by the excavation of a foundation pit with high accuracy. The authors will subsequently try to develop physics-based machine learning to optimize the prediction results of the model.  
Writing--review & editing, S.X.; Funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. The data used to support the findings of this study are included within the article. Grant No. 41472254 from the Natural Science Foundation of China is gratefully acknowledged. The authors declare no conflicts of interest.