Multi-objective optimization-based prediction of excavation-induced tunnel displacement

This paper proposes an inverse method for improving the prediction of tunnel displacements during adjacent excavation.In this framework,staged data assimilation and parameter identification are conducted using the multi-objective particle swarm optimization algorithm.Recent monitoring data are assumed to be more informative and assigned more weights in the multi-objective optimization to improve the prediction accuracy.Then,an empirical formula is applied to correct the time effect of tunnel displacement.The Kriging method is introduced to surrogate the finite element model to reduce computational cost.The proposed framework is verified using a typical staged“excavation nearing tunnel”case.The predictions using the updated parameters are in good agreement with the measurements.The identified values of underlying soil parameters are within the typical ranges for the unloading condition.The updated time effect indicates that tunnel displacements may develop excessively in the three months after the region S1-B is excavated to the bottom.The maximum vertical tunnel displacement may increase from the currently measured 12 mm to the calculated 26 mm if the later construction is suspended long enough.Subsequent constructions need to be timely conducted to restrain the time effect and control tunnel displacements.