Nonlinear constitutive models for FRP composites using artificial neural networks

2007 ◽  
Vol 39 (12) ◽  
pp. 1035-1042 ◽  
Author(s):  
Rami Haj-Ali ◽  
Hoan-Kee Kim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Constitutive Models ◽  
Frp Composites ◽  
Artificial Neural ◽  
Nonlinear Constitutive Models

scholarly journals Nonlinear multiscale simulation of elastic beam lattices with anisotropic homogenized constitutive models based on artificial neural networks

2021 ◽  
Author(s):  
Til Gärtner ◽  
Mauricio Fernández ◽  
Oliver Weeger
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lattice Structure ◽  
Constitutive Models ◽  
Multiscale Simulation ◽  
Multiscale Approach ◽  
Perturbation Approach ◽  
Material Symmetry ◽  
Highly Nonlinear ◽  
Artificial Neural

AbstractA sequential nonlinear multiscale method for the simulation of elastic metamaterials subject to large deformations and instabilities is proposed. For the finite strain homogenization of cubic beam lattice unit cells, a stochastic perturbation approach is applied to induce buckling. Then, three variants of anisotropic effective constitutive models built upon artificial neural networks are trained on the homogenization data and investigated: one is hyperelastic and fulfills the material symmetry conditions by construction, while the other two are hyperelastic and elastic, respectively, and approximate the material symmetry through data augmentation based on strain energy densities and stresses. Finally, macroscopic nonlinear finite element simulations are conducted and compared to fully resolved simulations of a lattice structure. The good agreement between both approaches in tension and compression scenarios shows that the sequential multiscale approach based on anisotropic constitutive models can accurately reproduce the highly nonlinear behavior of buckling-driven 3D metamaterials at lesser computational effort.

scholarly journals Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Jinxing Lai ◽  
Junling Qiu ◽  
Zhihua Feng ◽  
Jianxun Chen ◽  
Haobo Fan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rock Mass ◽  
Constitutive Models ◽  
Future Research ◽  
Dynamic Prediction ◽  
Regression Methods ◽  
Ann Models ◽  
Rock Mass Deformation ◽  
Artificial Neural

In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document