Dataset for: Multi-Fidelity deep learning for predicting the nonlinear buckling behaviour of concrete thin-shells
This dataset contains scripts and data supporting the research article, "Multi-Fidelity deep learning for predicting the nonlinear buckling behaviour of concrete thin-shells".
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 – Multi-Fidelity Multilayer Perceptrons – that have been trained to predict the nonlinear buckling factor 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.,
2026.
Dataset for: Multi-Fidelity deep learning for predicting the nonlinear buckling behaviour of concrete thin-shells.
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-01533.
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Data
models.zip
application/zip (832MB)
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 (164kB)
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 (8kB)
Software: MIT License
A Python script with classes that can be used to load the datasets.
metrics.py
text/x-script.python (5kB)
Software: MIT License
A Python script with error metrics classes.
models.py
text/x-script.python (1kB)
Software: MIT License
A Python script with the deep learning model classes.
tools.py
text/x-script.python (968B)
Software: MIT License
A Python script that contains some tool functions.
hf_MLP_varying_dataset_size.py
text/plain (6kB)
Creative Commons: Attribution 4.0
A Python script for the evaluation of the baseline high-fidelity MLPs trained with varying amounts of data.
lf_MLP_varying_dataset_size.py
text/plain (6kB)
Creative Commons: Attribution 4.0
A Python script for the evaluation of low-fidelity MLPs trained with varying amounts of data.
mf_sequential … dataset_size.py
text/plain (6kB)
Creative Commons: Attribution 4.0
A python script for the evaluation of Multi-Fidelity (sequential) MLPs trained with varying amounts of data.
mf_transfer … dataset_size.py
text/plain (6kB)
Creative Commons: Attribution 4.0
A Python script for the evaluation of Multi-Fidelity (transfer learning) MLPs trained with varying amounts of data.
train_hf_MLP.py
text/x-script.python (12kB)
Creative Commons: Attribution 4.0
A Python script for the nonlinear buckling prediction using the baseline high-fidelity MLP.
train_lf_MLP.py
text/x-script.python (12kB)
Creative Commons: Attribution 4.0
A Python script for linear buckling prediction using the Low-fidelity MLP.
train_mf_sequential_MLP.py
text/x-script.python (12kB)
Creative Commons: Attribution 4.0
A Python script for nonlinear buckling prediction using Multi-Fidelity (sequential) MLP.
train_mf_transfer_MLP.py
text/x-script.python (12kB)
Creative Commons: Attribution 4.0
A Python script for nonlinear buckling prediction using Multi-Fidelity (transfer learning) MLP.
Creators
Maxime Pollet
University of Bath
Paul Shepherd
University of Bath
Will Hawkins
University of Bath
Contributors
University of Bath
Rights Holder
Documentation
Data collection method:
The methods used to generate this data can be found in the related 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 new folder and extract the "models.zip" and "results.zip" folders inside. Additionally, create a "scripts" folder and store all Python scripts inside.
Methodology link:
Pollet, M., Shepherd, P., and Hawkins, W., 2026. Multi-Fidelity deep learning for predicting the nonlinear buckling behaviour of concrete thin-shells. Engineering Applications of Artificial Intelligence, 174, 114490. Available from: https://doi.org/10.1016/j.engappai.2026.114490.
Documentation Files
requirements.txt
text/plain (529B)
Creative Commons: Attribution 4.0
README.md
text/plain (7kB)
Creative Commons: Attribution 4.0
Funders
University of Bath
https://doi.org/10.13039/501100000835
Publication details
Publication date: 21 March 2026
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-01533
URL for this record: https://researchdata.bath.ac.uk/1533
Related papers and books
Pollet, M., Shepherd, P., and Hawkins, W., 2026. Multi-Fidelity deep learning for predicting the nonlinear buckling behaviour of concrete thin-shells. Engineering Applications of Artificial Intelligence, 174, 114490. Available from: https://doi.org/10.1016/j.engappai.2026.114490.
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)
