## 1 Introduction  
In recent years, with the continuous improvement of the density of China's comprehensive transportation network, the intersection nodes of highways and roads have become a key bottleneck restricting the efficiency and safety of the road network. Therefore, it is essential to transform level crossings between railways and highways into three-dimensional intersections, and rectangular top-tube tunnels under existing railways are a solution to this problem. During the construction of an underpass railway project, stress redistribution will occur in the surrounding soil, causing soil settlement and deformation, which in turn will cause deformation of the upper track and affect the safety of train operation [1, 2]. Therefore, it is necessary to predict the settlement of the track and roadbed caused by the construction process and take corresponding measures to control the settlement to ensure the safety of construction and railway operation [3, 4]. At present, there are two main methods used to study surface settlement in underpass projects. The first is the theoretical analysis method, which mainly includes the random medium theory [5] and analytical solution [6]. The second type is numerical analysis methods, which use finite element software to establish a simulation model to predict settlement [7]. Currently, these predictive methods and models are predominantly applied in isolation. Although each approach possesses distinct advantages and applicable scenarios,the absence of a multi-source data integration mechanism impedes the comprehensive extraction of complex correlation features embedded in field measurements, thereby limiting the improvement of the prediction accuracy. With the continuous progress of computational technology, an increasing number of researchers are leveraging the robust nonlinear modeling capabilities of advanced algorithms, such as deep neural networks and adaptive support vector regression, to address the challenging issue of settlement prediction in railway underpass projects [8; 9]. [PERSON] used artificial neural networks to predict the surface settlement above the tunnel of Line 2 of the Sao Paulo Metro [10]. In order to determine the appropriate model for predicting the maximum surface settlement caused by shield tunneling, [PERSON] et al. [11] compared the prediction effects of artificial neural network (ANN) and recurrent neural network (RNN). The results show that the RNN exhibits higher accuracy in modeling the spatiotemporal correlation of settlement sequences, but its inherent defect--namely, the exponential decay of gradients with increasing iteration steps (the gradient vanishing problem)--leads to a significant decrease in the learning efficiency of the model's dynamic response to long-term construction parameters. [PERSON]'s team proposed a method for predicting surface settlement during multi-stage excavation of subway tunnels based on the VMD-GRU hybrid model. This model can effectively capture the dynamic response characterized by sudden deformation fluctuations during the construction process by integrating variational mode decomposition and gated recurrent units [12]. To address the accuracy problem of settlement prediction during the operation phase of concrete face rockfill dam (CFRD), [PERSON] et al. proposed a multi-monitoring point collaborative modeling method based on optimized long short-term memory network (LSTM), which effectively integrated the spatiotemporal correlation characteristics and significantly improved the accuracy and stability of settlement prediction [13]. Traditional theoretical analysis, numerical simulation, and the above-mentioned combined neural network model have achieved certain results by predicting surface settlement data.However, these methods also have some shortcomings.