Seismic behavior analyses of reinforced tunnel with steel fiber reinforced concrete (SFRC) composites by consideration of optimum thickness in artificial neural network (ANN) analyses

Document Type : Research Paper

Authors
Hootan Fakharian 1
Hadi Bahadori 2
Mohamadali Ramezanpour 1
Ali Dehghanbanadaki 3
1 Department of Civil Engineering, Rudehen Branch, Islamic Azad University, Rudehen, Iran
2 Department of Civil Engineering, Urmia University, Urmia, Iran
3 Department of Civil Engineering, Damavand Branch, Islamic Azad University, Damavand, Iran
10.22075/ijnaa.2024.35531.5286
Abstract
This study is organized in two parts. In the first part, the seismic behaviour of tunnels reinforced with steel fibre-reinforced concrete composites (SFRC) fibre reinforcing is investigated and using the finite element method (FEM) in ABAQUS software, 3 different models of the thickness (similar in terms of compressive strength and tensile strength) have been evaluated in the Tehran Metro tunnel. In this section, to apply seismic force, the PGA characteristics of the El Centro earthquake in the United States have been used, and the parameters of force and displacement concerning time have been extracted. In the second part, using the effect of three different thicknesses of 20, 40 and 60 cm in determining the behaviour of the tunnel under the earthquake, the amount of stress and displacement over time was extracted. These results were investigated using two models of neural networks, MLP and RBF, to determine the optimal thickness to withstand stress and displacement in the tunnel. It was also found that the optimal thickness of the tunnel was obtained using MLP and RBF networks to control stress of 47.95 cm and 48.22 cm, respectively, and to control displacement of 47.21 cm and 48.15 cm, respectively. Finally, it can be acknowledged that using the optimal thicknesses, the maximum stress tolerance by MLP and RBF methods is equal to $1.7556 \times 10^{+ 6}\ N/m^{2}$ and $1.9289 \times 10^{+ 6}\ N/m^{2}$ is obtained. Also, the highest amount of displacement with both methods (due to the good accuracy of both models in determining the displacement) is equal to 0.0256 cm.
Keywords
Seismic Analyses
Reinforced Tunnel
Thickness
Neural Network
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International Journal of Nonlinear Analysis and Applications
Volume 16, Issue 11
November 2025
Pages 133-142

  • Receive Date 05 August 2024
  • Accept Date 04 November 2024