XGBoost and genetic programming methods for analysing the vitreous transition of the epoxy adhesive

Journal of Adhesion Science and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 26

Published: Jan. 16, 2025

Fibre-reinforced polymer (FRP) composites are increasingly favoured for strengthening existing structures due to their numerous structural benefits. Nevertheless, the performance of such technology is strongly affected by behaviour epoxy resin adhesive layer, which largely dependent on its curing conditions. This study introduces a deep learning (DL) framework that leverages eXtreme Gradient Boosting (XGBoost) and genetic programming (GP) comprehensively influence scenarios vitreous transition adhesive. An experimental dataset comprising 160 data points was used develop predictive models. The XGBoost models exhibited high accuracy both onset temperature peak tan δ temperature, achieving R2 values 0.982 0.993 unseen test set, respectively. While GP lower with 0.834 0.842, they provided explicit equations enhance interpretability DL model facilitate practical application. To make these insights accessible engineers without expertise, web-based graphical user interface software developed, incorporating all Additionally, feature assessment conducted, providing visual representations impact each output results, thus enhancing engineering applications.

Language: Английский

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Variations in viscoelastic properties of structural adhesives and strengthening performance across service scenarios

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 472, P. 140846 - 140846

Published: March 23, 2025

Language: Английский

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Low-code analysis of glass transition temperatures of structural strengthening adhesives

The Journal of Adhesion, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 28

Published: April 10, 2025

Language: Английский

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A Bayesian-physical informed conditional tabular generative adversarial network framework for low-carbon concrete data augmentation and hyperparameter optimization

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 152, P. 110811 - 110811

Published: April 17, 2025

Language: Английский

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Predictions of Mechanical Properties of Fiber Reinforced Concrete using Ensemble Learning Models

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 98, P. 110990 - 110990

Published: Oct. 12, 2024

Language: Английский

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Physics-informed data-driven Bayesian network for the risk analysis of hydrogen refueling stations

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 110, P. 371 - 385

Published: March 1, 2024

Language: Английский

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CTGAN in Augmentation of Radiomics Features Classification from Narrow Band Imaging for Laryngeal Cancer

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 5

Published: June 26, 2024

Language: Английский

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Genetic evolutionary deep learning for fire resistance analysis in fibre-reinforced polymers strengthened reinforced concrete beams

Engineering Failure Analysis, Journal Year: 2024, Volume and Issue: 169, P. 109149 - 109149

Published: Dec. 11, 2024

Language: Английский

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Low-Code Automl Solutions for Predicting Bond Strength and Failure Modes of Cfrp-Steel Joints

Published: Jan. 1, 2024

Language: Английский

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Low-code AutoML solutions for predicting bond strength and failure modes of CFRP-steel joints

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 449, P. 138420 - 138420

Published: Sept. 24, 2024

Language: Английский

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