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).