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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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 |
| Summary: | 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. |
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