Neural network-based material modeling

A neural network-based material modeling methodology for engineering materials is developed in this study. With this material modeling methodology, the stress-strain behavior of a material is captured within the distributed weight structure of a multilayer feedforward neural network trained directly on the stress-strain data obtained from experiments. The feasibility of this approach is verified through constructing neural network-based constitutive models of plain concrete in biaxial stress states and in uniaxial cyclic compression. A composite material model simulating the stress-strain behavior of reinforced concrete as a generic composite material in a biaxial stress state is built with experimental data from Vecchio and Collins' tests on reinforced concrete panels in both pure shear and combined shear with normal stresses.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Wu, Xiping

Contributors dc:contributor

• Ghaboussi, Jamshid

• Applied Mechanics
• Engineering, Civil
• Engineering, Materials Science
• Computer Science

Statement dc:rights

• Copyright 1991 Wu, Xiping

Language dc:language

eng

Identifier

AAI9211043
(UMI)AAI9211043

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/21588