Genetic Evolutionary Deep Learning for Fire Resistance Analysis in Frp-Strengthened Rc Beams

Published: Jan. 1, 2024

Fibre Reinforced Polymers (FRPs) have become increasingly popular for strengthening concrete structures due to their structural benefits. However, a major concern with FRP-strengthened members is poor fire resistance. This study introduces genetic evolutionary deep learning (DL) approach that utilises the LightGBM algorithm, enhanced Genetic Algorithm hyperparameter optimisation, alongside Programming (GP) assess resistance performance of strengthened reinforced (RC) beams. A substantial dataset comprising 20,000 data points, derived from numerically modelled results validated through experimental studies, underpins data-driven DL analyses. The model demonstrates high predictive accuracy time and deflection at failure RC beams, R2 values 0.923 0.789, respectively. Although GP shows lower (R2 0.642 0.643), it provides explicit equations facilitate deeper understanding ease application. graphical user interface software, incorporating these two models, has been developed enable engineers apply insights in practice without requiring coding skills. Furthermore, an assessment feature influences was conducted, visually depicting impact on output results, thus enhancing interpretability engineering applications.

Language: Английский

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Evaluation of Stress Distributions in Trimaterial Bonded Joints with Nano-Resin Adhesive Using Machine Learning Models

Results in Materials, Journal Year: 2024, Volume and Issue: 23, P. 100618 - 100618

Published: Aug. 24, 2024

Adhesive bonded joints hold significant importance across various industrial sectors in modern engineering, owing to their lightweight nature and myriad advantages. The rising demand for trimaterial underscores utility versatility. In these joints, the choice of materials both adherends greatly influences strength, structural reliability, overall characteristics. While numerous researches have extensively analyzed stress distributions, effects, behaviors, many relied on a one-factor-at-a-time approach, focusing solely individual design variables' effects. However, recognizing intricate interplay among material combinations collective impact performance, this study employs types White-box, Black-box, Grey-box machine learning algorithms identify an optimized ML model as well predict distributions any random upper lower adherend materials. Dataset total 178 were utilized training phases with 5-fold cross validation tuning. decision tree regressor emerged by comparing quantitative metrics accuracy benchmark prediction outcomes obtained through all models. maximum attained was impressive 99.97 %, while minimum recorded 89.74 %. This research aims tailored specifically where nano layer resin is adhesive.

Language: Английский

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Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability

BioData Mining, Journal Year: 2024, Volume and Issue: 17(1)

Published: Oct. 30, 2024

Cutaneous melanoma is the most aggressive form of skin cancer, responsible for cancer-related deaths. Recent advances in artificial intelligence, jointly with availability public dermoscopy image datasets, have allowed to assist dermatologists identification. While feature extraction holds potential detection, it often leads high-dimensional data. Furthermore, datasets present class imbalance problem, where a few classes numerous samples, whereas others are under-represented.

Language: Английский

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Deep learning assisted prediction on main factors influencing shear strength of sintered nano Ag-Al joints under high temperature aging

Engineering Failure Analysis, Journal Year: 2024, Volume and Issue: 167, P. 109028 - 109028

Published: Nov. 7, 2024

Language: Английский

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