Hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete
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 neu...
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| Format: | Article |
| Language: | English |
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Engineered Science Publisher
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/44555/ http://umpir.ump.edu.my/id/eprint/44555/1/Hybrid%20deep%20learning%20approach%20for%20accurate%20prediction%20of%20flowability.pdf |
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| author | Al-Hinawi, Ayat Mahmoud Alelaimat, Radwan A. Alhenawi, Esraa AlBiajawi, Mohammad Ismail Yousef |
| author_facet | Al-Hinawi, Ayat Mahmoud Alelaimat, Radwan A. Alhenawi, Esraa AlBiajawi, Mohammad Ismail Yousef |
| author_sort | Al-Hinawi, Ayat Mahmoud |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:55:47Z |
| format | Article |
| id | ump-44555 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:55:47Z |
| publishDate | 2024 |
| publisher | Engineered Science Publisher |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-445552025-05-21T08:59:13Z http://umpir.ump.edu.my/id/eprint/44555/ Hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete Al-Hinawi, Ayat Mahmoud Alelaimat, Radwan A. Alhenawi, Esraa AlBiajawi, Mohammad Ismail Yousef QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) 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. Engineered Science Publisher 2024 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/44555/1/Hybrid%20deep%20learning%20approach%20for%20accurate%20prediction%20of%20flowability.pdf 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) https://dx.doi.org/10.30919/es1182 https://dx.doi.org/10.30919/es1182 |
| spellingShingle | QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) Al-Hinawi, Ayat Mahmoud Alelaimat, Radwan A. Alhenawi, Esraa AlBiajawi, Mohammad Ismail Yousef Hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete |
| title | Hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete |
| title_full | Hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete |
| title_fullStr | Hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete |
| title_full_unstemmed | Hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete |
| title_short | Hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete |
| title_sort | hybrid deep learning approach for accurate prediction of flowability in ultra-high-performance concrete |
| topic | QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/44555/ http://umpir.ump.edu.my/id/eprint/44555/ http://umpir.ump.edu.my/id/eprint/44555/ http://umpir.ump.edu.my/id/eprint/44555/1/Hybrid%20deep%20learning%20approach%20for%20accurate%20prediction%20of%20flowability.pdf |