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Selection of Methodology for Neural Network Modeling of Constitutive Hystereses Behavior of Soils
Author(s) -
Basheer Imad A.
Publication year - 2000
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/0885-9507.00206
Subject(s) - artificial neural network , constitutive equation , idealization , computer science , plasticity , model selection , artificial intelligence , machine learning , biological system , engineering , finite element method , structural engineering , materials science , biology , physics , quantum mechanics , composite material
Classic constitutive modeling of geomaterials based on the elasticity and plasticity theories suffers from limitations pertaining to formulation complexity, idealization of behavior, and excessive empirical parameters. This article capitalizes on the modeling capabilities of neural networks as substitutes for the classic approaches. The neural network–based modeling overcomes the difficulties encountered in understanding the underlying microscopic processes governing the material's behavior by redirecting the efforts into learning the cause‐effect relations from behavioral examples. Several methodologies are presented and cross‐compared for effectiveness in approximating a theoretical hysteresis model resembling stress‐strain behavior. The most effective methodology was used in modeling the constitutive behavior of an experimentally tested soil and produced models that simulated the real behavior of the soil with high accuracy. Although these models are empirical, they are retrainable and thus, unlike classic constitutive modeling techniques, can be revised and generalized easily when new data become available.