Recursive Time Series Prediction Modeling of Long-Term Trends in Surface Settlement During Railway Tunnel Construction

The surface settlement of railroad tunnels is dynamically updated as the construction progresses, exhibiting complex nonlinear characteristics. The accuracy of the on-site nonlinear regression fitting prediction method needs to be improved. To prevent surface settlement and surrounding rock collapse during railroad tunnel construction, while also ensuring the safety of the tunnel and existing structures, we propose a recursive prediction model for the long-term trend of surface settlement utilizing a singular spectrum analysis (SSA), improved sand cat swarm optimization (ISCSO), and a kernel extreme learning machine (KELM). First, SSA decomposition, known for its adaptive decomposition of one-dimensional nonlinear time series, reorganizes the early surface settlement data. The dynamic sliding window method is introduced to construct the prediction dataset, which is then trained using the KELM. ISCSO is used to optimize the key parameters of the KELM to obtain the long-term trend curves of surface settlement through recursive time series prediction. The superiority and effectiveness of ISCSO and the model are verified through numerical experiments and simulation experiments based on engineering cases, providing a reference for the early warning and control of surface settlement during the construction of similar tunnels.