## 1 Introduction
The method utilises the Seagull Optimisation Algorithm (SOA) to optimise the hyperparameters of the neural network structure in the LSTM, mainly to improve the model. The analysis results show that the proposed model has the highest \(R^{2}\), as well as the smallest RMSE value and absolute relative error for the monitoring data of the four monitoring points. [PERSON] et al. [15] took advantage of convolutional neural networks (CNNs) to optimise LSTM models to improve the low learning effectiveness. [PERSON] et al. [16] added the attention mechanism to the LSTM model so that the model can pay more attention to the parameters with higher weights. A sensitivity analysis based on the Pearson correlation coefficient can improve the prediction efficiency and reduce irrelevant input parameters. It was found that the LSTM attention model has a higher accuracy compared to the LSTM model. Several other scholars have investigated the problem of time-series data prediction using combinatorial models. [PERSON] et al. [17] proposed a model of fully integrated empirical modal decomposition with adaptive noise long- and short-term memory (CEEMDAN-LSTM model); CEEMDAN divides one-dimensional data into multidimensional data by a model of fully integrated empirical modal decomposition. Experimental results show that this deep-learning method performs better compared to existing machine-learning techniques and algorithms. [PERSON] and [PERSON] [5] proposed a combined deep-learning model for pollutant concentration prediction, and found that the combined model can enhance the generalisation ability of the model. [PERSON] and [PERSON] [18] developed a sequence-to-sequence mechanism combined with a bidirectional LSTM to predict the inflow rate. The results show that the model has a good prediction accuracy. [PERSON] et al. [19] used a deep-pocketed bidirectional LSTM neural network with a self-attention mechanism to capture the temporal dependencies of the original sensor data, and the method can deal with various missing data scenarios in a dam monitoring system. [PERSON] and [PERSON] and [PERSON] (2020) proposed a method to predict the settlement deformation of deep foundation pits based on the combination model of complementary ensemble empirical mode decomposition (CEEMD) and LSTM, and the results showed that the combination model has better prediction results. [PERSON] et al. [PERSON] et al. (2021) proposed a hybrid spatio-temporal depth prediction model with both principal and spatio-temporal blocks. Experiments showed that the spatio-temporal relationship between the surface settlement data obtained from different monitoring points within the network is dynamic during metro pit excavation, and the STdeep model exhibits the best performance and excellent stability. [PERSON] et al. [PERSON] et al. (2021) used the particle swarm algorithm (PSO) to optimise the number of neurons, dropout, and batch-size in the structure of LSTM network, and found that the PSO-LSTM model can accurately describe the dynamic evolution process of the buried pipelines, and has a better prediction accuracy than the improved Gaussian curve method and the LSTM neural network model. At present, although some theoretical models have been established for predicting the impact of pit construction on building and surface settlement, the pit construction process is cumbersome, there are many working conditions, and different working conditions have different impacts on the buildings around the pit; at the same time, there are many parameters in the existing surface settlement prediction model, which makes it difficult to capture the key information of the settlement data. Therefore, there is a need to establish a dynamic prediction model for building settlement with a higher accuracy, fewer parameters, and clear working conditions.