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Parameter Inversion of Constitutive Model of Soil Using Neural Networks

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Book cover Advances in Computer Science, Intelligent System and Environment

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 105))

Abstract

Neural network models are developed for estimating model parameters of conditioned soils in EBP shield. The parameter identification of nonlinear constitutive model of soil mass is based on an inverse analysis procedure, which consists of minimizing the objective function representing the difference between the experimental data and the calculated data of the mechanical model. The models are found to have good predictive ability and are expected to be very useful for estimating model parameters of conditioned soils in EBP shield.

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Author information

Authors and Affiliations

  1. The second affiliated Hospital of Dalian Medical University, Dalian, 116027, China

    Jizhe Wang

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

    Shouju Li, Juan Cui & Lintao Man

Authors
  1. Jizhe Wang
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  2. Shouju Li
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  3. Juan Cui
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  4. Lintao Man
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Editor information

Editors and Affiliations

  1. Wuhan Section Special No.1, International Science & Education Researcher Association, Jiangxia Road of Wuhan, Wuhan, China

    David Jin

  2. Guangzhou Section, International Science & Education Researcher Association, No.144, Jinheng Road Jinbi Garden 85-1102 southern Road of Industry Avenue Haizhu District, Guangzhou, Guang Dong provience, China

    Sally Lin

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© 2011 Springer-Verlag Berlin Heidelberg

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Wang, J., Li, S., Cui, J., Man, L. (2011). Parameter Inversion of Constitutive Model of Soil Using Neural Networks. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23756-0_67

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  • DOI: https://doi.org/10.1007/978-3-642-23756-0_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23755-3

  • Online ISBN: 978-3-642-23756-0

  • eBook Packages: EngineeringEngineering (R0)

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