###### Abstract  
The construction of deep foundation pits in subway stations can affect the settlement of existing buildings adjacent to the pits to varying degrees. In this paper, the Long Short-Term Memory neural network prediction model of building settlement caused by deep foundation pit construction was established using the monitoring data of building settlement around a deep foundation pit project in a metro station in Shanghai, and appropriate hyperparameters including batch size and training set ratio were determined. The accuracy of settlement prediction for single-point and multi-point monitoring of buildings was analyzed. Meanwhile, the effects of construction parameters, engineering geological parameters, and spatial parameters on the accuracy of building settlement prediction were investigated. The results show that the batch size and training set proportion can be taken as 16 and 60%, respectively, when using the Long Short-Term Memory neural network prediction model. The proposed Long Short-Term Memory network model can stably predict the settlement of buildings adjacent to deep foundation pits. The accuracy of settlement prediction at a single point of a building (80%) is lower than the accuracy of coordinated prediction at multiple points (88%). More accurate settlement prediction is achieved with the total reverse construction method. The more detailed the consideration of working conditions, geological parameters, and spatial parameters, the better. The evaluation metrics of the prediction model, RMSE, MAE, and \(R^{2}\), were 0.57 mm, 0.65 mm, and 0.91, respectively. The results of this paper have some practical reference value for analyzing the settlement of buildings caused by foundation pit works. Long Short-Term Memory neural network; construction conditions; settlement prediction; geotechnical parameters; spatial parameters 2024 14 2143  
excavation is mainly related to the foundation type and the distance between the building and the pit [9]. Based on the numerical simulation methods, the numerical model of vertical inhomogeneous settlement of existing buildings during pit excavation was established based on PLAXIS software with 2D model and a two-dimensional model considering the plane strain-stress state, and it was found that pit excavation has a significant effect on the stability of the buildings, which provides a more reliable prediction of the building settlement [10; 11]. However, the analysis of field-measured data is relatively homogeneous, and numerical analysis has been criticized for its accuracy and validity due to the fact that creating and validating finite element models is very time-consuming and sensitive to boundary conditions [12]. Building settlement monitoring data belong to time-series data, the traditional prediction model is complicated, the model parameters are difficult to determine, and the dynamic prediction of settlement is not effective. Existing studies usually take the surface settlement as the settlement of the surrounding buildings. Simplified equations for predicting settlement were proposed by simplified assessment of building damage caused by pit excavation [13]. The results of predicting building settlement using a non-isometric grey model are better than other grey models, with a 30% improvement in fitting results over the original model [14; 15]. Deep learning can process a large amount of time-series data quickly and has been used by many scholars in recent years for the settlement calendar change problem. The use of neural networks to predict the settlement of a single footh on soft soil reinforced with rigid inclusions revealed that the important input parameters in the network are cohesion, angle of internal friction, and Young's modulus [16]. The settlement of shallow foundations on gypsum soils was predicted using artificial neural networks, and convergence of predicted values with true values was found [17].