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Artificial neural network for stress–strain behavior of sandy soils: Knowledge based verification

Authors
Journal
Computers and Geotechnics
0266-352X
Publisher
Elsevier
Publication Date
Volume
32
Issue
5
Identifiers
DOI: 10.1016/j.compgeo.2005.06.002
Keywords
  • Artificial Neural Network
  • Sensitivity Analysis
  • Stress–Strain
  • Sandy Soils
  • Undrained Behavior
Disciplines
  • Computer Science
  • Mathematics

Abstract

Abstract In this paper, artificial neural networks (ANNs) are applied to model the stress–strain behavior of in situ sandy soils containing nonplastic fines. A main drawback of these types of models is discussed, i.e. an ANN based model gives no information how the model inputs affect the output. A systematic approach is therefore presented to acquire and verify the stored knowledge of a general ANN based constitutive soil model. Sensitivities of the output to corresponding inputs are defined mathematically. A sensitivity analysis is then performed to extract the dominant rules of the proposed model, which compare favorably with experimental observations.

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