Dataset for "Fast structural analysis of concrete thin-shells using deep learning"
This dataset contains scripts and data supporting the following research article: Pollet, M., Shepherd, P., Hawkins, W., and Costa, E., 2026. Fast structural analysis of concrete thin-shells using deep learning. Computers & Structures, 320, 108042.
Concrete thin-shells are materially efficient structures, which can be used to reduce the environmental impact of concrete structures. Their shape is typically determined iteratively and evaluated through Finite Element Analysis (FEA). This research proposes the use of surrogate models as faster alternatives to FEA, thus enabling wider design space exploration.
This dataset contains deep learning models – Multilayer Perceptrons, Convolutional Neural Networks, and Graph Neural Networks – that have been trained to predict the buckling factor and stress fields of concrete thin-shells of various shapes under design loads. It also contains the Python scripts that were used to train these models and assess their performance. Running these scripts necessitates the associated ConcreteShellFEA dataset to be downloaded. Further details about this data can be found in the related research article.
Cite this dataset as:
Pollet, M.,
Shepherd, P.,
Hawkins, W.,
Costa, E.,
2026.
Dataset for "Fast structural analysis of concrete thin-shells using deep learning".
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-01504.
Export
Data
models.zip
application/zip (213MB)
Creative Commons: Attribution 4.0
This folder contains the trained deep learning models used to achieve the predictive performances highlighted in the research article. Files formats: PyTorch (.pth).
results.zip
application/zip (3GB)
Creative Commons: Attribution 4.0
This folder contains the predictions made by the deep learning models, which were used to calculate used to the predictive performances highlighted in the research article. Files formats: PyTorch (.pth).
Code
datasets.py
text/x-script.python (11kB)
Software: MIT License
A Python script with classes that can be used to load the datasets.
metrics.py
text/x-script.python (6kB)
Software: MIT License
A Python script with error metrics classes.
models.py
text/x-script.python (14kB)
Software: MIT License
A Python script with the deep learning model classes.
tools.py
text/x-script.python (11kB)
Software: MIT License
A Python script that contains some tool functions.
train_CNN_buckling.py
text/x-script.python (13kB)
Software: MIT License
A Python script that can be used to train and evaluate a CNN for buckling prediction.
train_CNN_stress.py
text/x-script.python (16kB)
Software: MIT License
A Python script that can be used to train and evaluate a CNN for stress prediction.
train_GNN_buckling.py
text/x-script.python (13kB)
Software: MIT License
A Python script that can be used to train and evaluate a GNN for buckling prediction.
train_GNN_stress.py
text/x-script.python (14kB)
Software: MIT License
A Python script that can be used to train and evaluate a GNN for stress prediction.
train_MLP_buckling.py
text/x-script.python (12kB)
Software: MIT License
A Python script that can be used to train and evaluate a MLP for buckling prediction.
train_MLP_stress.py
text/x-script.python (15kB)
Software: MIT License
A Python script that can be used to train and evaluate a MLP for stress prediction.
Creators
Maxime Pollet
University of Bath
Paul Shepherd
University of Bath
Will Hawkins
University of Bath
Eduardo Costa
University of Bath
Contributors
University of Bath
Rights Holder
Documentation
Data collection method:
Full details of the methodology used may be found in the associated article.
Technical details and requirements:
The data in the models and results folders was generated using the Python code in scripts folder. These scripts rely on the dependencies listed in requirements.txt.
Additional information:
The original folder structure is given in README.md. To reproduce it, create a folder "FastStructuralAnalysisOfConcreteThinShellsUsingDeepLearning" and extract the "models.zip" and "results.zip" folders inside. Additionally, create a "scripts" folder and store all Python scripts inside. The path to the ConcreteShellFEA dataset needs to be specified in each script, under the DATASET_ROOT variable.
Methodology link:
Pollet, M., Shepherd, P., Hawkins, W., and Costa, E., 2026. Fast structural analysis of concrete thin-shells using deep learning. Computers & Structures, 320, 108042. Available from: https://doi.org/10.1016/j.compstruc.2025.108042.
Documentation Files
requirements.txt
text/plain (3kB)
Creative Commons: Attribution 4.0
README.md
text/plain (5kB)
Creative Commons: Attribution 4.0
Funders
University of Bath
https://doi.org/10.13039/501100000835
PhD studentship
Publication details
Publication date: 9 February 2026
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-01504
URL for this record: https://researchdata.bath.ac.uk/1504
Related papers and books
Pollet, M., Shepherd, P., Hawkins, W., and Costa, E., 2026. Fast structural analysis of concrete thin-shells using deep learning. Computers & Structures, 320, 108042. Available from: https://doi.org/10.1016/j.compstruc.2025.108042.
Related datasets and code
Pollet, M., Shepherd, P., Hawkins, W., and Costa, E., 2026. ConcreteShellFEA: A surrogate modelling dataset for the buckling and stress behaviour of concrete thin-shells. Version 1. Bath: University of Bath Research Data Archive. Available from: https://doi.org/10.15125/BATH-01519.
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Contact person: Maxime Pollet
Faculty of Engineering & Design
Architecture & Civil Engineering
Research Centres & Institutes
Centre for Digital, Manufacturing & Design (The Foundry)
