Real-time prediction system for tunnel deformation induced by excavation and its application.

To enable real-time prediction of tunnel deformation induced by foundation pit excavation, this study develops a predictive framework that integrates an analytical solution based on a two-stage unloading method with a back-propagation neural network for parameter identification. The analytical model accounts for excavation-induced stress redistribution using Mindlin's solution, while the tunnel is modeled as a beam resting on a double-sided elastic foundation to represent its deformation behavior. Five key mechanical parameters are considered for inversion, including the initial residual stress coefficient, unloading reduction coefficient, longitudinal stiffness reduction factor, and the vertical and horizontal subgrade reaction moduli. These parameters are calibrated using field monitoring data through a trained back-propagation neural network. The proposed framework is embedded into a real-time prediction system and applied to the Gubei Road Station of Shanghai Metro Line 15, which is located adjacent to Line 10. The predicted tunnel displacements exhibit strong agreement with field measurements, with maximum relative errors of 21.82% in the vertical direction and 1.95% in the horizontal direction. Except during phases of displacement trend reversal, the system consistently maintains high predictive accuracy. These results verify the reliability of the proposed method for forecasting excavation-induced tunnel deformation and underscore its applicability in proactive risk management for urban tunneling projects.