Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach

To control tunneling risk, the prediction of the surface settlement rate induced by shield tunneling using earth pressure balance plays a crucial role. To achieve this, ten independent variables were identified that can affect the amount of settlement. The nonlinear relationship between maximum ground surface settlements and ten influential independent variables was considered in artificial neural network (ANN) models. A total of 150 genuine datasets derived from the Southern Development Section of the Tehran Metro Line 6 project were used to train, validate, and test ANN techniques. Hence, the ground surface settlements of the mentioned project were predicted by the most accurate back propagation ANN technique. Ultimately, the importance level of different influential parameters on ground settlement at tunneling is relatively determined based on the results of the optimal neural network. The results used in this paper to evaluate the relative importance of each variable involved in the rate of ground surface settlement demonstrate that the parameters of grout injection and permeability equivalent to the proportions of approximately 16.91% and 5.07% have the highest and lowest impact, successively.

Automated summary

To control tunneling risk, the prediction of surface settlement induced by shield tunneling is crucial. Artificial neural network (ANN) models were used to consider the nonlinear relationship between maximum ground settlements and influential variables. The most accurate back propagation ANN technique was applied to predict ground surface settlements.