Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm
This paper introduces a novel methodology for predicting Unconfined Compressive Strength (UCS) in rocks by integrating Support Vector Regression (SVR) with two cutting-edge optimization algorithms: the Seahorse Optimizer (SO) and the COOT Optimization Algorithm (COOT). Unlike traditional UCS predict...
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| Format: | Article |
| Language: | English |
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Bilijipub publisher
2024-12-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_212432_e4c2aee397acd0798ae9f8644aea0610.pdf |
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| author | Rafiqul Islam Md. Arif Hossain |
| author_facet | Rafiqul Islam Md. Arif Hossain |
| author_sort | Rafiqul Islam |
| collection | DOAJ |
| description | This paper introduces a novel methodology for predicting Unconfined Compressive Strength (UCS) in rocks by integrating Support Vector Regression (SVR) with two cutting-edge optimization algorithms: the Seahorse Optimizer (SO) and the COOT Optimization Algorithm (COOT). Unlike traditional UCS prediction methods that often struggle with slow convergence and local minima entrapment, this approach leverages the nonlinear modeling capabilities of SVR and enhances its performance through advanced optimizers. The SVSH (SVR+SO) and SVCO (SVR+COOT) models were developed and evaluated using a comprehensive dataset of rock samples with UCS measurements. Comparative analysis demonstrates that the proposed models not only achieve significantly higher prediction accuracy but also exhibit faster convergence compared to standalone SVR. These results underscore the potential of the hybrid SVR-optimizer models to set a new benchmark in UCS prediction, offering greater precision and computational efficiency. Among them, the SVSH models had the maximum accuracy with an excellent R2 value of 0.998 and a scanty RMSE value of 1.261. Therefore, the results confirm that the proposed SVSO model is a promising tool to be used by engineering and geological professionals. This ensures a very strong and reliable UCS prediction method that is vital for the improvement of civil engineering project safety and efficiency. |
| format | Article |
| id | doaj-art-4389d0c189784c5da69e6f19ba952e5c |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-4389d0c189784c5da69e6f19ba952e5c2025-02-12T08:48:16ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-12-0100304829410.22034/aeis.2024.486651.1250212432Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning AlgorithmRafiqul Islam0Md. Arif Hossain1Department of Civil Engineering, College of Sylhet Engineering, Sylhet, BangladeshDepartment of Civil Engineering, Ahsanullah University of Science and Technology, Dhaka, BangladeshThis paper introduces a novel methodology for predicting Unconfined Compressive Strength (UCS) in rocks by integrating Support Vector Regression (SVR) with two cutting-edge optimization algorithms: the Seahorse Optimizer (SO) and the COOT Optimization Algorithm (COOT). Unlike traditional UCS prediction methods that often struggle with slow convergence and local minima entrapment, this approach leverages the nonlinear modeling capabilities of SVR and enhances its performance through advanced optimizers. The SVSH (SVR+SO) and SVCO (SVR+COOT) models were developed and evaluated using a comprehensive dataset of rock samples with UCS measurements. Comparative analysis demonstrates that the proposed models not only achieve significantly higher prediction accuracy but also exhibit faster convergence compared to standalone SVR. These results underscore the potential of the hybrid SVR-optimizer models to set a new benchmark in UCS prediction, offering greater precision and computational efficiency. Among them, the SVSH models had the maximum accuracy with an excellent R2 value of 0.998 and a scanty RMSE value of 1.261. Therefore, the results confirm that the proposed SVSO model is a promising tool to be used by engineering and geological professionals. This ensures a very strong and reliable UCS prediction method that is vital for the improvement of civil engineering project safety and efficiency.https://aeis.bilijipub.com/article_212432_e4c2aee397acd0798ae9f8644aea0610.pdfrocksunconfined compressive strengthsupport vector regressionseahorse optimizercoot optimization algorithm |
| spellingShingle | Rafiqul Islam Md. Arif Hossain Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm Advances in Engineering and Intelligence Systems rocks unconfined compressive strength support vector regression seahorse optimizer coot optimization algorithm |
| title | Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm |
| title_full | Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm |
| title_fullStr | Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm |
| title_full_unstemmed | Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm |
| title_short | Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm |
| title_sort | unconfined compressive strength prediction of rocks using a novel hybrid machine learning algorithm |
| topic | rocks unconfined compressive strength support vector regression seahorse optimizer coot optimization algorithm |
| url | https://aeis.bilijipub.com/article_212432_e4c2aee397acd0798ae9f8644aea0610.pdf |
| work_keys_str_mv | AT rafiqulislam unconfinedcompressivestrengthpredictionofrocksusinganovelhybridmachinelearningalgorithm AT mdarifhossain unconfinedcompressivestrengthpredictionofrocksusinganovelhybridmachinelearningalgorithm |