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| Autores principales: | , , |
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| Formato: | Recurso digital |
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| Publicado: |
Zenodo
2026
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| Acceso en línea: | https://doi.org/10.5281/zenodo.20322665 |
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- <p>This repository contains the source code, datasets, and supporting materials associated with the manuscript titled <em>“Physics-Integrated Machine Learning Framework for Predicting Torsional Strength and Response Behavior of Reinforced Concrete Beams,”</em> submitted to the journal <em>Engineering Applications of Artificial Intelligence</em>.</p> <p>The archived materials support the development, validation, and reproducibility of a hybrid artificial intelligence framework for torsional strength prediction in reinforced concrete beams. The framework integrates physics-guided neural networks (PGNN), graph neural networks (GNN), and a Bayesian-optimized stacking ensemble (BO-Stack) to provide accurate, interpretable, and physically consistent predictions of torsional behavior.</p> <p>Contents of this repository include:<br>• Experimental torsional test dataset<br>• Physics-informed synthetic dataset generated using variational autoencoder (VAE)-based augmentation<br>• PGNN implementation<br>• GNN implementation<br>• BO-Stack ensemble framework<br>• SHAP-based interpretability scripts<br>• Torsional boundary/failure domain generation scripts<br>• Cross-validation and benchmarking scripts<br>• Graphical user interface (GUI) for engineering application<br>• Reproducibility documentation and execution instructions</p> <p>The repository is intended to ensure transparency, reproducibility, and public accessibility of the computational framework and supporting data associated with this study.</p>