A Neural Network Approach for Estimating Model Parameters of Rockfill Materials

Ji Zhe Wang; Ying Feng Su

doi:10.4028/www.scientific.net/AMR.671-674.167

Paper Titles

Test Research on the Long-Term Consolidation Mechanism of Ultra-Soft Clay
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The Degradation of Saturated Clay’s Un-Drained Shearing Deformation Modulus under Cyclic Loading
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The Relationship between High Fluorine Groundwater and Geological Environment in Gaomi
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Underlying Surface Influenced on the Landslide Runout
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A Neural Network Approach for Estimating Model Parameters of Rockfill Materials
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A Study on the Effect of Initial Shear Stress on Dynamic Properties of Saturated Clay
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Application of GA-BP to Back Analysis of Rock’s Parameters
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Application Study on the Stability of Xiufengsi Landslide by Using Limit Equilibrium Method and Finite Element Method
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Back-Analysis for Design Parameters of Large Diameter Bored Piles
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 671-674A Neural Network Approach for Estimating Model...

A Neural Network Approach for Estimating Model Parameters of Rockfill Materials

Abstract:

The Duncan-Change constitutive model of rockfill materials is used to characterize the behaviors of modeled rockfill materials. A parameter estimation procedure based on neural network is developed. The material parameters for the rockfill materials are determined from the experimental results of triaxial compression. The investigation results reveal that the identified model parameters can provide satisfactory prediction of the behavior of the rockfill materials tested.

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Periodical:

Advanced Materials Research (Volumes 671-674)

Pages:

167-170

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.671-674.167

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Online since:

March 2013

Authors:

Ji Zhe Wang, Ying Feng Su

Keywords:

Constitutive Model, Estimating Model Parameters, Neural Network (NN), Rockfill Materials

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