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Intelligent Forecast Procedures for Slope Stability with Evolutionary Artificial Neural Network

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Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

Evolutionary artificial neural network is applied for the prediction of the slope stability from the geotechnical material properties and the slope geometries. Coupling the genetic algorithm with artificial neural network, an effective forecast procedure is presented to analyze slope stability. In order to deal with the local minimal problem of artificial neural network with Back-Propagation rule, the connection weights of the artificial neural network are changed by using the genetic algorithm during the iteration process. The practical application demonstrates that the forecast of slope stability using artificial neural network is feasible and a well trained artificial neural network reveals an extremely fast convergence, a better generalization and a high degree of accuracy in the intelligent forecast for the slope stability.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China

    Shouju Li & Yingxi Liu

Authors
  1. Shouju Li
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  2. Yingxi Liu
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Li, S., Liu, Y. (2004). Intelligent Forecast Procedures for Slope Stability with Evolutionary Artificial Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_127

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_127

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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