Abstract
A BP neural network model with 4 × 10 × 2 three-layer is developed to predict the maximum Mises stress and horizontal deformation of circular tunnels subjected to earthquake loadings. The four input common factors F1–F4 are extracted from 12 input parameters which represent the characteristics of tunnel liner, surrounding soil and earthquake characteristics. After training and testing of 70 sets of literature data, three earthquake motions are applied to the tunnel of Guangzhou Metro Line 4 as parametric case study. BP ANN and ABAQUS FEA results are compared and found in general agreement with relative error within 15%. Hence, the method based on BP ANN has a certain guiding significance for practical engineering and provides a new approach for the seismic analysis of tunnels.
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Huo, H., Zhou, L., Wang, Y., Zhang, T. (2021). A Method for Predicting Seismic Stress and Deformation of Circular Tunnels Based on BP Artificial Neural Network. In: Barla, M., Di Donna, A., Sterpi, D. (eds) Challenges and Innovations in Geomechanics. IACMAG 2021. Lecture Notes in Civil Engineering, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-030-64518-2_44
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DOI: https://doi.org/10.1007/978-3-030-64518-2_44
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