Abstract:The variation of axial force in foundation pit steel supports is a crucial indicator that reflects the changes in internal force within foundation pit. It is a key research focus in disaster prevention and mitigation in foundation pit engineering. Due to the complexity of soil mechanical properties and the uncertainty of stress evolution, it is challenging to capture the variation of internal force in foundation pit solely through monitoring and calculation. Previous studies have shown that the evolution of support axial force exhibits typical temporal characteristics, suggesting that temporal sequence models can be used for predictive analysis. However, the accuracy of these predictions is generally low. Additionally, significant spatial correlations exist in the axial force variation at multiple locations in founda-tion pit, yet existing models often fail to capture spatial information. To address the issues above, this study establishes a model that combines Graph Convolutional Neural Network (GCN) and Long Short Term Memory (LSTM), which is a spatiotemporal prediction model capable of capturing both temporal and spatial features of the data. The model constructed an adjacency matrix based on the spatial information of actual locations and generated corresponding spatial features. By utilizing axial force, spatial information, and temperature as input parameters, this model predicted the trend of support axial force. Predictive analysis was conducted using data from four spatially correlated points in a metro station in Shanghai, and the results of the combined model were compared with actual measurements and predictions based on the LSTM model. The results indicated that: (1) The combined model exhibited a faster convergence speed, stronger fitting capabilities for long-period data, and better responsiveness to data volatility; (2) The accuracy of the combined model surpassed that of the LSTM model that only considered temporal sequence. The combined model effectively enhances the prediction accuracy of axial force data, providing computational references for numerical predictions in engineering applications.