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Earth slope reliability analysis under seismic loadings using neural network

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Abstract

A new method was proposed to cope with the earth slope reliability problem under seismic loadings. The algorithm integrates the concepts of artificial neural network, the first order second moment reliability method and the deterministic stability analysis method of earth slope. The performance function and its derivatives in slope stability analysis under seismic loadings were approximated by a trained multi-layer feed-forward neural network with differentiable transfer functions. The statistical moments calculated from the performance function values and the corresponding gradients using neural network were then used in the first order second moment method for the calculation of the reliability index in slope safety analysis. Two earth slope examples were presented for illustrating the applicability of the proposed approach. The new method is effective in slope reliability analysis. And it has potential application to other reliability problems of complicated engineering structure with a considerably large number of random variables.

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, 410083, Changsha, China

    Peng Huai-sheng, Deng Jian PhD (Professor) & Gu De-sheng

  2. China Non-ferrous Engineering and Research Institute, 100038, Beijing, China

    Peng Huai-sheng

Authors
  1. Peng Huai-sheng
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  2. Deng Jian PhD
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  3. Gu De-sheng
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Corresponding author

Correspondence to Deng Jian PhD.

Additional information

Foundation item: Projects (50404010; 50574098) supported by the National Natural Science Foundation of China; project (05jj10010) supported by the Hunan Provincial Natural Science Foundation of Distinguished Young Scholars

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Peng, Hs., Deng, J. & Gu, Ds. Earth slope reliability analysis under seismic loadings using neural network. J Cent. South Univ. Technol. 12, 606–610 (2005). https://doi.org/10.1007/s11771-005-0131-9

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  • DOI: https://doi.org/10.1007/s11771-005-0131-9

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