Stress Analysis of Segmental Tunnel Lining Using Artificial Neural Network

The paper describes an artificial neural network method (ANNM) to predict the stresses executed on segmental tunnel lining. An ANN using multi-layer perceptron (MLP) is developed. At first, database resulted from numerical analyses was prepared. This includes; depth of cover (H), horizontal to vertical stress ratio (K), thickness of segment (t), Young modulus of segment (E) and key segment position in each ring (.) on the tunnel perimeter as input variables. Different types of stresses and extreme values of displacement have been considered as output parameters. Sensitivity analysis showed that the cover of the tunnel and key position are the most and less effective input variables on output parameters, respectively. Results for coefficient of determination (R-2), variance accounted for (VAF), coefficient of efficiency (CE) and root mean squared error (RMSE) illustrates a high accuracy of the presented ANN model to predict the stress types and displacements of segmental tunnel lining.