Estimation of load for tunnel lining in elastic soil using physics-informed neural network

Abstract

A reverse calculation method termed soil and lining physics-informed neural network (SL-PINN) is proposed for the estimation of load for tunnel lining in elastic soil based on radial displacement measurements of the tunnel lining. To achieve efficient and accurate calculations, the framework of SL-PINN is specially designed to consider the respective displacement characteristics of surrounding soil and tunnel lining. A multistep training method based on the meshless characteristics of SL-PINN is established to promote calculation efficiency. The multistep training method involves increasing the number of collocation points in each calculation step while decreasing the learning rate after scaling of SL-PINN. The feasibility of SL-PINN is verified by numerical simulation data and field data. Compared to other inverse calculation methods, SL-PINN has lower precision requirements for the measurement instrument with the same level of calculation accuracy.