## 1 Introduction  
At present, the number of foundation pits is increasing, and most of them are found in the busy areas of cities. The stability of the pit excavation itself and its potential impact on the surrounding buildings and structures have become a primary consideration in engineering construction [1]. A large number of studies have been carried out on the impact of pit construction on the settlement of surrounding buildings, and the commonly used methods include regression analysis, grey theory, time-series analysis, neural network analysis, and other [2] methods. However, these methods have certain limitations: regression analysis belongs to a static data processing method, while the pit construction monitoring data are dynamic data; grey theory has high data requirements, requiring the data to show an exponential increase or monotonicity, while the pit monitoring data do not have monotonicity, mostly presenting complex nonlinear characteristics; and time-series analysis is a statistical method, and the use of extrapolation time cannot be too long; Among the commonly used neural network analysis methods, the back propagation (BP) neural network is the most widely used, but the BP neural network is greatly affected by the initial value, slower convergence, longer iteration time, and being prone to local extreme value problems. Currently, some scholars use deep-learning models to study the impact of pit construction on the settlement of surrounding buildings. Among the deep-learning models, the long short-term memory (LSTM) network [3] has a large advantage in temporal data processing, which can overcome the problem of gradient explosion and gradient disappearance in the process of back propagation, and can effectively capture both long-term and short-term information [4], and can carry out dynamic feature extraction for successively related data, and has been used in machine translation, information retrieval, image processing, text recognition [5], intelligent question and answer, speech recognition, and data prediction [6], and other fields, and has also found some applications in the field of geotechnical engineering. LSTM is a gated recurrent neural network that adds a gating mechanism to control the transmission of information in a neural network based on a simple recurrent neural network [7; 8; 9]. [PERSON] et al. [10] applied the deep-learning algorithm LSTM to predict the long-term settlement of the reclamation settlement, and applied the LSTM-based model to the reclamation of Kansai International Airport (KIA) and Chek Lap Kok Airport (CLKA). The results show that the LSTM-based model can accurately capture the long-term settlement of KIA. The developed model has a great generalisation capability and can be directly applied to other projects. [PERSON] et al. [11] found LSTM can be successfully used to predict consolidation settlements and provide highly accurate predictions for soft ground construction engineering. [PERSON] et al. [12] explored the effects of random errors and environmental parameters on the prediction of dam settlement, and proposed a prediction model based on a multi-input LSTM network and random error extraction. With the settlement data of concrete-faced rock fill dams, the analysis showed that the removal of random errors can significantly improve the short-term prediction performance, and the consideration of environmental parameters can significantly improve the long-term prediction performance. [PERSON] et al. [13] established four different machine-learning methods (the radial basis function (RBF) neural network, back propagation neural network (BPNN), generalised regression neural network (GRNN), and Gaussian prior (GP) regression model) for settlement prediction, and it was found that the GP model had the best prediction performance. Many scholars have attempted to optimise LSTM models using various methods. [PERSON] et al. [14] introduced an improved LSTM model and a weighted Markov model to predict shuce deformation.