## 5 Conclusions  
This study systematically outlines the application process of a machine-learning-based method for accurately predicting the horizontal displacement of the foundation pit's enclosure structure. Several related factors, including geological environment, construction progress, stress, and displacement, were employed for the HD prediction task. Six mainstream machine-learning algorithms were selected to test the ability of HD prediction. Based on a dataset of 38,966, the random forest prediction model proved to be the best choice after 5-fold cross validation. The test results showed that the R\({}^{2}\) exceeded 0.99, and the MAE was less than 1, indicating the model's acceptable and satisfactory predictive performance for the specific project. In the prediction task, parameter D (depth) has the greatest influence on the prediction, the enclosures show a consistent deformation pattern over time, and the pattern is correlated to the depth of enclosure of the foundation pit. In addition to the water level, the surface settlement and support axial force have less influence on the machine-learning prediction.  
Moreover, the study applied the Pearson correlation coefficient to analyze the factor correlation and screen a new dataset. However, compared with the original datasets, the screened features could not reach the accuracy of the original data.  
Finally, to further validate the validity of this method, data from different periods of several monitoring sites in the field were selected for validation. The result proves the feasibility of machine learning in HD prediction as well.  
By fully considering the influence factor of deformation of a deep foundation-pit enclosure, machine learning can predict the deformation of foundation-pit enclosure in construction, which is of great significance for construction safety. This method can be extended in the construction of deep foundation pits in a similar environment.  
Research LimitationsDue to the irregularities in the amount of data and the quality of the engineering data from the construction site of the project, some of the characterization factors, such as building inclination and earth pressure, which are supposed to play an essential role in the deformation mechanism of the enclosure structure, could not be echoed with the rest of the data due to the incomplete data of the individual compiled dataset. More work should be done to collect similar data and create a database to provide more characterization factors.  
Conceptualization, Y.X. and J.S.; methodology, Y.Z.; software, Q.J.; validation, W.J., C.T. and Q.J.; formal analysis Y.Z.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.X.; writing--review and editing, J.S.; supervision, J.S.; project administration, J.S.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by China Coal Technology and Industry Group Co., Ltd. Science and Technology innovation start-up capital.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. The dataset used is available on GitHub at [[https://github.com/Arnold0624/My-Database](https://github.com/Arnold0624/My-Database)]([https://github.com/Arnold0624/My-Database](https://github.com/Arnold0624/My-Database)).  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Wuhan Design & Research Institute Co., Ltd. of China Coal Technology & Engineering Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Assessment Indicators** & \(\mathbf{R^{2}}\) & **RMSE (mm)** & **MAE (mm)** \\ \hline Prediction of CX16 CX58 & 0.9646 & 3.6403 & 0.5625 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy assessment of predicted values at CX-1 and CX-58 monitoring sites.