Dataset for “Interpretable ANN-Based Constitutive Modeling for Quasi-Brittle Materials from Synthetic FEM Data”

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.”