A Neural Network Model for Evaluating Gravel Liquefaction Using Dynamic Penetration Test

Qing Xu; Fei Kang; Jun Jie Li

doi:10.4028/www.scientific.net/AMM.275-277.2620

Paper Titles

Existence and Stability of Periodic Solution for Impulsive Hopfield Cellular Neural Networks with Distributed Delays
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Determining Side-Cores of Plastic Moldings
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The Analysis and Improvement of Inverse Problem Simulated Annealing Algorithm about Concrete Temperature Field
p.2611

Discussion of the Construction Design Methodology Considering both Evolutionary Structural Optimization and 3D Printing Technology
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A Neural Network Model for Evaluating Gravel Liquefaction Using Dynamic Penetration Test
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Three-Dimensional Modeling of Open-Pit Based on AutoCAD and its Application
p.2624

Using Interpolation Points to Rectify Cutter Paths of Five-Axis Machining
p.2629

Automatic Side-Cores Construction in Injection Plastic Mold Design for Free-Form NURBS Surface Models
p.2635

Starting Energy Dissipation of Traffic Flow with On-Ramp
p.2640

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 275-277A Neural Network Model for Evaluating Gravel...

A Neural Network Model for Evaluating Gravel Liquefaction Using Dynamic Penetration Test

Abstract:

Evaluation of liquefaction potential of soils is important in geotechnical earthquake engineering. Significant phenomena of gravelly soil liquefaction were reported in 2008 Wenchuan earthquake. Thus, further studies on the liquefaction potential of gravelly soil are needed. This paper investigates the potential of artificial neural networks-based approach to assess the liquefaction potential of gravelly soils form field data of dynamic penetration test. The success rates for occurrence and non-occurrence of liquefaction cases both are 100%. The study suggests that neural networks can successfully model the complex relationship between seismic parameters, soil parameters, and the liquefaction potential of gravelly soils.