The paper describes an artificial neural network method (ANNM)

The main objective of this paper is to optimize the geometrical and engineering characteristics of concrete segments of tunnel lining using Finite Element (FE) based Response Surface Methodology (RSM). Input data for RSM statistical analysis were obtained using FEM. In RSM analysis, thickness (t) and elasticity modulus of concrete segments (E), tunnel height (H), horizontal to vertical stress ratio (K) and position of key segment in tunnel lining ring (θ) were considered as input independent variables. Maximum values of Mises and Tresca stresses and tunnel ring displacement (UMAX) were set as responses. Analysis of variance (ANOVA) was carried out to investigate the influence of each input variable on the responses. Second-order polynomial equations in terms of influencing input variables were obtained for each response. It was found that elasticity modulus and key segment position variables were not included in yield stresses and ring displacement equations, and only tunnel height and stress ratio variables were included in ring displacement equation. Finally optimization analysis of tunnel lining ring was performed. Due to absence of elasticity modulus and key segment position variables in equations, their values were kept to average level and other variables were floated in related ranges. Response parameters were set to minimum. It was concluded that to obtain optimum values for responses, ring thickness and tunnel height must be near to their maximum and minimum values, respectively and ground state must be similar to hydrostatic conditions.

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