A Model Based on Neural Network to Predict Surface Settlement During Subway Station Construction: A Case Study of the Dongba-Zhongjie Station in Beijing, China

Surface settlement prediction is crucial to assess the safety of subway station construction. To overcome challenges such as missing on-site settlement data and a limited number of monitoring points, this study proposes a composite prediction model that integrates finite element analysis, a time-series interval GA-BP neural network, and variational mode decomposition (VMD) techniques. Using the Dongba-zhongjie Station in Beijing Subway Line 3 as a case study, surface settlement predictions were made for both typical monitoring points and randomly selected feature points throughout the construction period, followed by validation. The experimental results show that the root mean square error (RMSE) of the finite element model is 15.77%, confirming the model’s effectiveness. As excavation progressed through the second underground floor and bottom plate, the settlement at the maximum settlement point began to rebound, and the structure tended to stabilize. At this stage, construction of the comprehensive utility tunnel above the station can proceed concurrently.