## 6 Conclusions  
In this paper, using the monitoring data of the settlement of the surrounding buildings triggered by the pit construction, an artificial neural network prediction model LSTM-RA-ANN (which stands for long short-term memory, residual, and attention-based artificial neural network), which integrates the attention mechanism and the residual updating, was established to analyse the prediction performance of the model for building settlement under three types of pit construction conditions, such as enclosure construction, earth excavation construction, and basement support construction, and obtained the following conclusions:  
(1) The evaluation indices of the constructed LSTM-RA-ANN model are closely related to the construction conditions, and the average values of RMSE for the enclosure construction, earth excavation construction, and basement support construction are 0.50, 1.60, and 0.51, the average values of MAE are 0.46, 1.34, and 0.40, and the average values of \(R^{2}\) are 0.89, 0.97, and 0.78, respectively, and the prediction accuracy of the great-small order corresponds to the following: basement support, enclosure construction, and earthwork excavation, respectively.  
(2) When the LSTM-RA-ANN model is used to predict the building settlement, the average values of RMSE, MAE, and \(R^{2}\) of the model without inputting the soil layer parameter in the input layer are 0.37, 0.34, and 0.87, respectively; the presence or absence of the soil layer parameter in the input layer does not have a significant effect on the overall performance of the model.  
(3) The larger the distance between the monitoring point and the edge of the deep foundation pit, the better the model prediction performance: when the distance from the  
Figure 11: Model evaluation indices for different buildings using various numbers of monitoring points: (**a**) RMSE; (**b**) MAE; and (**c**) \(R^{2}\).  
edge of the deep foundation pit is increased by 5 m, the RMSE decreases by 5%, the MAE decreases by 6%, and the \(R^{2}\) increases by 2%; when the distance from the edge of the deep foundation pit is increased by 10 m, the RMSE decreases by 26%, the MAE decreases by 17%, and the \(R^{2}\) increases by 2%.  
(4) The activation function, the initial learning rate, and the maximum iteration batch have a greater influence on the prediction and evaluation indices of the LSTM-RA-ANN model; compared with the input single-point monitoring values, the average values of RMSE, MAE, and \(R^{2}\) of the model change to 0.92, 0.90, and 1.10 times, respectively, when the input multi-point monitoring values are inputted, which has a slightly higher prediction accuracy but not much change in the overall performance.  
[PERSON]: made substantial contributions to the conception and design of the work, and the acquisition, analysis, and interpretation of data, and the creation of new software used in the work; and drafted the work or revised it critically for important intellectual content. [PERSON]: approved the version to be published; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the first author on request.  
The authors declare no conflicts of interest.