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    <responseDate>2026-10-15T08:25:54Z</responseDate>
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                <identifier>oai:data.mendeley.com/6hkhws6b8s.2</identifier>
                <datestamp>2026-08-10T14:43:25Z</datestamp>
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            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Figueiredo, Álefe</dc:creator>
    <dc:title>Dataset for “Interpretable ANN-Based Constitutive Modeling for Quasi-Brittle Materials from Synthetic FEM Data”</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains synthetic stress–strain histories generated from nonlinear finite element simulations and used for the development and assessment of an Artificial Neural Network-based Constitutive Model (ANN-CM) for quasi-brittle materials, with concrete adopted as the representative material.

The repository includes numerical data from several structural and material configurations, including plate simulations under different uniaxial, biaxial, shear, and tension-compression stress states, as well as bending, shear, diametrical compression, pre-stressed bending, asymmetric traction, and L-shaped panel tests. Selected plate simulations were also performed with different rotation angles to expand the range of stress-state orientations represented in the database.

The data are organized into training and independent assessment subsets. Each record corresponds to an integration-point state and contains the current stress–strain state, two previous stress–strain states, the imposed strain increment, and the corresponding stress increment used as the ANN-CM training target. Under plane-stress conditions, the learning problem comprises 21 input variables and 3 output stress-increment variables.

The repository also includes a README, a data dictionary, and a file manifest describing the variable definitions, units, simulation naming conventions, and the role of each file.

These data support the study “Interpretable ANN-Based Constitutive Modeling for Quasi-Brittle Materials from Synthetic FEM Data” and the associated M.Sc. dissertation “The challenges of concrete constitutive modeling via artificial neural networks.”</dc:description>
    <dc:subject>Engineering</dc:subject>
    <dc:subject>Artificial Intelligence</dc:subject>
    <dc:subject>Machine Learning</dc:subject>
    <dc:subject>Finite Element Method</dc:subject>
    <dc:subject>Constitutive Modeling</dc:subject>
    <dc:subject>Quasi-Brittle Material</dc:subject>
    <dc:subject>Concrete Structure</dc:subject>
    <dc:subject>Explainable Artificial Intelligence</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/6hkhws6b8s.2</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/6hkhws6b8s.2</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/6hkhws6b8s</dc:relation>
    <dc:date>2026-08-10T14:43:25Z</dc:date>
</oai_dc:dc></metadata>
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