Data · dataset · 2026
Dataset for “Interpretable ANN-Based Constitutive Modeling for Quasi-Brittle Materials from Synthetic FEM Data”
Listed in Teesside University Research Data Repository
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.
Description
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.
Read the rest (1 more)
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.”
Links
Where it is published
- DOI doi.org/10.17632/6hkhws6b8s.2 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Artificial intelligence · Computer Science & AI · Earth & Environmental Science · Engineering · Machine learning
- Inferred from text
- Simulation 75%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/6hkhws6b8s.2 | 7 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |