Al-Hinawi, Ayat Mahmoud and Alelaimat, Radwan A. and Alhenawi, Esraa and AlBiajawi, Mohammad Ismail Yousef (2024) Hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete. Engineered Science, 30 (1182). pp. 1-17. ISSN 2576-988X. (Published)
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Abstract
By implementing several machine learning (ML), deep learning (DL), and hybrid deep learning models, the research methodology included a systematic approach, which included data separation, exploratory data analysis (EDA), artificial neural networks (ANN), K-Nearest neighbors (knn), convolutional neural networks (CNN), long short-term memory (LSTM), Gated recurrent units (GRU), and convolutional neural network long short-term memory/gated recurrent units hybrid models. Also, the mean absolute error (MAE), R-squared (R2), and Root Mean Square Error (RMSE) were utilized to evaluate these models. Our results demonstrate that hybrid deep learning models, specifically the CNN-GRU configuration, achieve better performance in predicting ultra-high-performance concrete (UHPC) flowability compared to individual Deep Learning models and traditional Machine Learning approaches. The CNN-GRU model exhibited the best predictive accuracy with a RMSE of 1.360066 and MAE of 1.036573. Additionally, feature selection techniques enhanced the performance of certain models, with the feature-selected random forest model showing notable improvements in accuracy, achieving an RMSE of 1.032841 and MAE of 0.767066. Infrastructure durability and building processes can be improved with higher Ultra-High-Performance Concrete flowability prediction, which improves the effectiveness of various operations of the UHPC mixture design and benefits the application.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Accurate prediction; Flowability; Hybrid deep learning; Ultra-high-performance concrete |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Civil Engineering Technology |
| Depositing User: | Mrs. Nurul Hamira Abd Razak |
| Date Deposited: | 21 May 2025 08:59 |
| Last Modified: | 21 May 2025 08:59 |
| URI: | http://umpir.ump.edu.my/id/eprint/44555 |
| Download Statistic: | View Download Statistics |
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