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        <identifier>oai:www.ideals.illinois.edu:2142/21588</identifier>
        <datestamp>2023-07-10</datestamp>
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        <thesis xmlns="http://www.ndltd.org/standards/metadata/etdms/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.ndltd.org/standards/metadata/etdms/1.1/ http://www.ndltd.org/standards/metadata/etdms/1.1/etdms11.xsd http://purl.org/dc/elements/1.1/ http://www.ndltd.org/standards/metadata/etdms/1.1/etdmsdc.xsd">
          <dc:contributor>Ghaboussi, Jamshid</dc:contributor>
          <dc:creator>Wu, Xiping</dc:creator>
          <dc:date>2011-05-07T13:13:07Z</dc:date>
          <dc:date>2011-05-07T13:13:07Z</dc:date>
          <dc:date>10000-01-01</dc:date>
          <dc:date>1991</dc:date>
          <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.</dc:description>
          <dc:description>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.</dc:description>
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  Previous issue date: 1991</dc:description>
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Item is restricted indefinitely.</dc:description>
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Original Data
Group with Access UIUC Users [automated]
Release Date: none
Reason: ETDs are only available to UIUC Users without author permission</dc:description>
          <dc:description>ETDs are only available to UIUC Users without author permission</dc:description>
          <dc:description>U of I Only</dc:description>
          <dc:identifier>AAI9211043</dc:identifier>
          <dc:identifier>(UMI)AAI9211043</dc:identifier>
          <dc:identifier>http://hdl.handle.net/2142/21588</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>Copyright 1991 Wu, Xiping</dc:rights>
          <dc:subject>Applied Mechanics</dc:subject>
          <dc:subject>Engineering, Civil</dc:subject>
          <dc:subject>Engineering, Materials Science</dc:subject>
          <dc:subject>Computer Science</dc:subject>
          <dc:title>Neural network-based material modeling</dc:title>
          <dc:type>text</dc:type>
          <degree>
            <department>Civil and Environmental Engineering</department>
            <discipline>Civil Engineering</discipline>
            <grantor>University of Illinois at Urbana-Champaign</grantor>
            <level>Dissertation</level>
            <name>Ph.D.</name>
          </degree>
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