Dataset for "Fast structural analysis of concrete thin-shells using deep learning"

Abstract

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.Full details of the methodology used may be found in the associated article.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.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