# A framework for assessing the impact of geometric imperfections in concrete shell structures using deep learning

## Overview

This dataset contains scripts and data supporting the following thesis:
Pollet, M. (2025). Rapid structural analysis of prefabricated thin concrete shells using deep learning (Thesis). University of Bath.

It contains the trained Multilayer Perceptrons and Convolutional Neural Networks used to obtain the results presented in the thesis, as well as Python scripts that were used to train these models and assess their performance.
Running these scripts necessitates the ConcreteShellFEA dataset (available at: https://doi.org/10.15125/BATH-01519) to be downloaded.
Additionally the path to this dataset needs to be specified in each script, under the DATASET_ROOT variable.
Further details about this data can be found in the related thesis.

## Repository structure

```
AFrameworkForAssessingTheImpactOfGeometricImperfectionsInConcreteThinShellsUsingDeepLearning/
│── models/                              # Trained models
│   ├── CNN_buckling_best_model.pth      # Buckling prediction CNN used in paper
│   ├── CNN_stress_best_model.pth        # Stress prediction CNN used in paper
│   ├── MLP_buckling_best_model.pth      # Buckling prediction MLP used in paper
│   ├── MLP_stress_best_model.pth        # Stress prediction CNN used in paper
│── results/raw_predictions/             # Predictions on testing set
│   ├── CNN_buckling_best_preds.pth      # Predictions made by buckling CNN
│   ├── CNN_stress_best_preds.pth        # Predictions made by stress CNN
│   ├── MLP_buckling_best_preds.pth      # Predictions made by buckling MLP
│   ├── MLP_stress_best_preds.pth        # Predictions made by stress MLP
│── scripts/                             # Scripts for model training and evaluation
│   ├── datasets.py                      # Definition of dataset classes
│   ├── metrics.py                       # Definition of metric classes
│   ├── models.py                        # Definition of model classes
│   ├── tools.py                         # Definition of various tool classes and functions
│   ├── train_CNN_buckling.py            # Buckling prediction using CNN
│   ├── train_CNN_stress.py              # Stress prediction using CNN
│   ├── train_GNN_buckling.py            # Buckling prediction using GNN
│   ├── train_GNN_stress.py              # Stress prediction using GNN
│   ├── train_MLP_buckling.py            # Buckling prediction using MLP
│   ├── train_MLP_stress.py              # Stress prediction using MLP
│── ordering_indices.csv                 # A list of indices to order dataset samples as in the article
│── requirements.txt                     # List of dependencies
│── README.md                            # Repository documentation
```

## License

The contents of this dataset are licensed under Creative Commons: Attribution 4.0 (CC BY), which allows for the derivation of new resources from the data, and for the redistribution of the data and their derivations, both openly and commercially, as long as the creators of this repository are credited for the original creation.

## Crediting/citing the creators of this dataset

The creators of this repository can be acknowledged by citing the related thesis:

Pollet, M. (2025). Rapid structural analysis of prefabricated thin concrete shells using deep learning (Thesis). University of Bath.

or using BibTeX:

```
@phdthesis{Pollet2025,
   author = {Maxime Pollet},
   school = {University of Bath},
   title = {Rapid structural analysis of prefabricated thin concrete shells using deep learning},
   year = {2025}
}
```