Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
An intelligent neural system for predicting structural response subject to earthquakes
Advances in Engineering Software
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Neural networks as material models within a multiscale approach
Computers and Structures
Presence: Teleoperators and Virtual Environments
Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data
Computers and Structures
Analysis of behaviour of soils under cyclic loading using EPR-based finite element method
Finite Elements in Analysis and Design
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Numerical implementation of EPR-based material models in finite element analysis
Computers and Structures
Integration of feedforward neural network and finite element in the draw-bend springback prediction
Expert Systems with Applications: An International Journal
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Neural network (NN) constitutive model adjusts itself to describe given stress and strain relationship. It is capable of capturing complex material behavior, using stress and strain sets from experiments. This paper presents a rate-dependent NN constitutive model formulation and its implementation in finite element analysis. The proposed NN model is verified for a standard solid viscoelasticity model. The model is then applied to analysis of time-dependent behavior of concrete. The proposed model has potential of capturing any rate-dependent material models, provided enough data sets are given. The issue of what constitutes a sufficient data set to train a neural network constitutive model must be addressed in future research.