## 1 Introduction
For example, traditional theoretical analysis methods are usually based on idealized assumptions, and it is difficult to effectively characterize the dynamic coupling effect between complex geological structures and construction parameters [14]; although numerical simulation technology can better reflect the actual engineering, its computational efficiency is limited by the selection of refined soil constitutive models, and large-scale three-dimensional simulation faces significant computing resource consumption [15]; in the field of machine learning, the classical support vector machine algorithm is subject to the quadratic programming theoretical framework, and there is a bottleneck of a sharp increase in computational complexity when processing high-dimensional and large-sample engineering data [16]. Therefore, it is urgent to optimize and upgrade existing methods. Current research is mostly limited to the static analysis of post-construction settlement results, and fails to effectively integrate dynamic construction stage settlement data, while existing railway facilities are extremely sensitive to real-time settlement responses.By incorporating time-series settlement data within the construction period into the training set, the temporal and spatial correlation laws of soil deformation can be deeply explored, and the dynamic data characteristics can be more comprehensively extracted, which can significantly improve the prediction accuracy of the model for complex settlement processes, thereby providing a scientific basis for real-time regulation during the construction process. By comparing and analyzing the existing neural network models in the field of sequence modeling, it is found that the Mamba model is a representative of the new state space model (SSM) [17]. With its efficient long-term dependency capture capability and linear complexity characteristics, it can effectively handle nonlinear dynamic relationshipsin time series and capture cross-scale data dependencies. This model has been widely used in fields such as semantic segmentation [18], image classification [19], remote sensing image prediction [20], and meteorological sequence construction [21]. Based on the above research, this paper proposes a Bi-Mamba subgrade settlement prediction model based on the Mamba model. The model first performs feature conversion on the time point of each variable. The Mamba variable correlation (VC) encoding layer then uses bidirectional Mamba to encode the correlation between variables and mine the information between global variables; then, weighted fusion is performed through the attention gate unit. Finally, the temporal dependency (TD) encoding layer containing a simple feedforward network (FFN) extracts the time-dependent features and outputs the prediction results. The Mamba model has shown great potential in the field of text and image modalities, but research in the field of subgrade settlement prediction is still blank. This paper is based on the Bi-Mamba model and the new Ningbo Yuanyi Road underpass under the existing railway project. The real-time monitoring data during the construction period is integrated to carry out subgrade settlement modeling and prediction, and the LSTM, iTransformer, Tide, and DLinear models are used for comparative analysis to verify the reliability of the Bi-Mamba model in the settlement prediction task.