Rheological parameter estimation for the prediction of long-term deformations in conventional tunnelling

The long-term deformations of mountain tunnels, which attract more and more attentions, are closely related to the time-dependent features of the surrounding rock mass. However, it is not easy to determine an appropriate rheological model and its corresponding parameters for a certain engineering instance. This paper presents a rheological parameter estimation technique by using error backpropagation neural network (BN) and genetic algorithm (GA). The application of the proposed technique to an engineering instance, Ureshino tunnel line I on Nagasaki expressway, is expatiated in detailed. The stochastic nature of the proposed technique is also discussed through case studies. It is proved that the proposed technique can provide the engineer with an optimal estimation of the rheological parameters, which can help the prediction of long-term deformations of mountain tunnels in the future.