## 1 Introduction  
With the rapid development of the nation and society, the urbanization process has accelerated, leading to a significant increase in the utilization of underground space [1, 2]. In foundation pit engineering, the scale and depth of deep excavations continue to grow, often situated in densely populated urban centers with complex and variable surroundings [3, 4]. These characteristics greatly amplify the challenges and risks associated with construction. Any accidents involving deep foundation pits can result in substantial socioeconomic losses and pose serious threats to the safety of construction workers and nearby structures [5, 6]. In deep foundation pit construction, the axial force of assembly steel struts is a critical indicator of the stability of the support system, directly reflecting the overall safety of the excavation [7]. However, it is significantly influenced by various external factors, including complex environmental conditions and varying excavation scenarios, with weather changes being particularly prominent [8; 9; 10]. For instance, during rain or snow, drastic temperature fluctuations can cause thermal expansion and contraction of metal materials, leading to axial force deviations around the design value [11; 12; 13]. Similarly, excavation processes, such as changes in excavation depth or speed, can introduce additional variability in axial forces. In extreme cases, these deviations may become substantial, potentially exceeding safe limits and increasing the risk of excavation instability and construction hazards [14; 15]. To address this challenge, it is essential to conduct in-depth research on the behavior of axial forces in assembly steel struts under varying environmental and excavation conditions, enabling precise prediction and early warning of potential issues. Such advancements can help to mitigate the risk of safety incidents caused by abnormal axial forces, reduce casualties, enhance the overall efficiency and reliability of deep foundation pit projects, and allow experts ample time to make informed decisions. In prediction methods, machine learning and neural networks have been widely applied for forecasting and early warning in engineering monitoring data. Support Vector Machines (SVMs) [16] and Random Forests (RFs) [17] have been used for predicting foundation pit deformation and safety risks. Backpropagation (BP) [18] neural networks and Artificial Neural Networks (ANNs) [19] have been employed for foundation pit settlement prediction. Furthermore, researchers have improved these basic networks to enhance prediction accuracy. For instance, [PERSON] et al. [20] optimized BP neural networks using the Grey Verhulst model, while [PERSON] et al. [21] introduced genetic algorithms to optimize ANN models, achieving higher accuracy in settlement prediction. Despite these advancements, such methods struggle to adequately capture the dynamic variations in monitoring data under complex working conditions. In recent years, the development of deep learning has led to the application of recurrent neural networks, such as Long Short-Term Memory (LSTM) [22] and Gated Recurrent Unit (GRU) [23], for predicting engineering monitoring data due to their strengths in handling time-series data. [PERSON] et al. [24] proposed an LSTM-based method for foundation pit deformation prediction, while [PERSON] et al. [25] enhanced the GRU model by incorporating a Variational Mode Decomposition (VMD) method optimized with Particle Swarm Optimization (PSO), resulting in the VMD-PSO-GRU model, which achieved high prediction accuracy. However, the stability of foundation pit support systems is influenced not only by temporal factors but also by spatial distribution characteristics, geological conditions, and external environmental factors such as temperature variations. The traditional methods have not considered the joint modeling of temporal and spatial dimensions.