DOI: 10.15125/bath-01519
title: ConcreteShellFEA: A surrogate modelling dataset for the buckling and stress behaviour of concrete thin-shells

description: ConcreteShellFEA is a dataset designed for the training of deep learning models to predict buckling loads and stress fields in concrete thin-shell structures.

It contains 3 smaller datasets, which can be used for different use cases:

1. PerfectShell_LinearFEA: A dataset of 20,000 thin-shells (with various span, height, thickness, and Young's modulus), for which buckling factors and stress fields under design loads were determined using linear Finite Element analysis. The data is presented in three formats (tabular, image, graph) to enable different types of deep learning models (Multilayer Perceptrons, Convolutional Neural Networks, and Graph Neural Networks) to be trained.

2. ImperfectShell_LinearFEA: A dataset of 20,000 imperfect thin-shells (with various span, height, thickness, Young's modulus, and geometric imperfections), for which buckling factors and stress fields under design loads were determined using linear Finite Element analysis. The data is presented in two formats (tabular, image) to enable different types of deep learning models (Multilayer Perceptrons, Convolutional Neural Networks) to be trained.

3. PerfectShell_NonlinearFEA: A dataset of 25,000 thin-shells (with various span, height, thickness, and Young's modulus, and geometric imperfections), for which buckling factors under design loads were determined using Finite Element analysis. The buckling factors were determined using linear Finite Element analysis for 20,000 thin-shells, and using nonlinear Finite Element analysis for 5,000 thin-shells, to enable mixed-fidelity applications.  The data is presented in a single format (tabular).
description: The methods used to generate this dataset can be found in the associated thesis.
description: The input data and output stress data are in HDF5 (.h5), and the output buckling data is in CSV (.csv). The input images are saved in HDF5 (.h5), and the input scalars in CSV (.csv). The graphs are saved in the PyTorch format (.pt).

The data was generated by conducting Finite Element simulations using the ANSYS software (version 2021 R1). The simulations' configurations are detailed in the associated thesis.

The Python scripts included in the dataset (load_tabular_datasets.py, load_image_datasets.py, and load_graph_datasets.py) rely on the dependencies listed in requirements.txt.
description: The original folder structure is given in README.md. To reproduce it, create a folder "ConcreteShellFEA" with two subfolders, "datasets" and "scripts". Extract the contents of the datasets ZIP folders (datasets-01.zip to datasets-28.zip) in the "datasets" folder and move the three Python scripts (load_tabular_datasets.py, load_image_datasets.py, and load_graph_datasets.py) inside the "scripts" folder.
description: Further relevant documentation may be found in the following resources.

   Pollet, M.,  2025.   Rapid structural analysis of prefabricated thin concrete shells using deep learning: (Alternative Format Thesis).     Thesis (PhD).    University of Bath.   Available from: https://researchportal.bath.ac.uk/en/studentTheses/rapid-structural-analysis-of-prefabricated-thin-concrete-shells-u.