{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/21588"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/21588","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Neural network-based material modeling","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Wu, Xiping"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Ghaboussi, Jamshid"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T13:13:07Z","date_published":"2011-05-07T13:13:07Z","updated_at":"2026-07-22T22:25:18Z","subjects":["Applied Mechanics","Engineering, Civil","Engineering, Materials Science","Computer Science"],"languages":["eng"],"rights":["Copyright 1991 Wu, Xiping"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9211043","(UMI)AAI9211043"],"render_values":[{"text":"AAI9211043","href":null,"code":true},{"text":"(UMI)AAI9211043","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/21588","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ghaboussi, Jamshid"]},{"key":"dc:creator","label":"Author","values":["Wu, Xiping"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T13:13:07Z","10000-01-01","1991"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Applied Mechanics","Engineering, Civil","Engineering, Materials Science","Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1991 Wu, Xiping"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9211043","(UMI)AAI9211043","http://hdl.handle.net/2142/21588"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","An adaptive neural network simulator is developed by implementing a dynamic node creation scheme and a higher order learning algorithm. Representation schemes, network architectures, training and testing methods, stress- and strain-based approaches for material modeling are investigated. An elastic unloading mechanism is studied with a concrete material model in biaxial compression. Main issues concerning the implementation of neural network material models in finite element solution procedures are briefly discussed. The results on the stress-strain relations of a material predicted by a neural network-based model are compared with experimental data. The developed approach shows promise in the constitutive modeling of composite materials.","Made available in DSpace on 2011-05-07T13:13:07Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9211043.pdf: 7142092 bytes, checksum: dbf9a12ea0ecb1ddb89a80a1930438e6 (MD5) Previous issue date: 1991","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:51:48Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:23:49-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["Neural network-based material modeling"]}]}],"canonical_facts":{"dc:contributor":["Ghaboussi, Jamshid"],"dc:creator":["Wu, Xiping"],"dc:date":["2011-05-07T13:13:07Z","10000-01-01","1991"],"dc:description":["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.","An adaptive neural network simulator is developed by implementing a dynamic node creation scheme and a higher order learning algorithm. Representation schemes, network architectures, training and testing methods, stress- and strain-based approaches for material modeling are investigated. An elastic unloading mechanism is studied with a concrete material model in biaxial compression. Main issues concerning the implementation of neural network material models in finite element solution procedures are briefly discussed. The results on the stress-strain relations of a material predicted by a neural network-based model are compared with experimental data. The developed approach shows promise in the constitutive modeling of composite materials.","Made available in DSpace on 2011-05-07T13:13:07Z (GMT). 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