The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength
The undrained shear strength (USS) of soil is essential in diverse structural engineering applications, including the design of earth dams, rock fills, foundations, highways, railways, and slope stability analysis. Traditional empirical and theoretical methods for estimating USS based on field tests...
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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_212430_b13d77df8994556c83dff1ec5969674e.pdf |
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| author | Rami Al-Qasimi Firas Al-Hajri |
| author_facet | Rami Al-Qasimi Firas Al-Hajri |
| author_sort | Rami Al-Qasimi |
| collection | DOAJ |
| description | The undrained shear strength (USS) of soil is essential in diverse structural engineering applications, including the design of earth dams, rock fills, foundations, highways, railways, and slope stability analysis. Traditional empirical and theoretical methods for estimating USS based on field tests often rely on correlation assumptions, leading to imprecise results. These conventional strategies are also limited in terms of efficiency, both in time and cost. To address these limitations, this study introduces innovative machine learning techniques, employing the Support Vector Regression (SVR) model to accurately predict USS. To enhance model performance, three meta-heuristic optimization algorithms Differential Squirrel Search Algorithm (DSSA), Golden Section Search Optimization (GSSO), and Northern Goshawk Optimization (NGO) were utilized. The frameworks were trained using four key input metrics: plastic limit (PL), liquid limit (LL), sleeve friction (SF), and overburden weight (OBW). The performance of the proposed frameworks was evaluated using five criteria: R², RMSE, MSE, SI, and SMAPE. Among the three hybrid frameworks developed for estimating USS, the SVR optimized with the Northern Goshawk Optimization (SVNG) algorithm outperformed the others, achieving the lowest RMSE value of 39.30 in the testing step and the highest R² value of 0.9980 in the testing step. These results demonstrate the superiority of the SVNG model in providing precise and efficient predictions of USS, surpassing conventional methods. |
| format | Article |
| id | doaj-art-568a0f5f592c406aaf803d4181a398e7 |
| 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-568a0f5f592c406aaf803d4181a398e72025-02-12T08:48:16ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-12-0100304496610.22034/aeis.2024.489661.1253212430The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear StrengthRami Al-Qasimi0Firas Al-Hajri1Department of Civil and Environmental Engineering, University of Qatar, Doha, QatarDepartment of Civil and Architectural Engineering, University of Qatar, Doha, QatarThe undrained shear strength (USS) of soil is essential in diverse structural engineering applications, including the design of earth dams, rock fills, foundations, highways, railways, and slope stability analysis. Traditional empirical and theoretical methods for estimating USS based on field tests often rely on correlation assumptions, leading to imprecise results. These conventional strategies are also limited in terms of efficiency, both in time and cost. To address these limitations, this study introduces innovative machine learning techniques, employing the Support Vector Regression (SVR) model to accurately predict USS. To enhance model performance, three meta-heuristic optimization algorithms Differential Squirrel Search Algorithm (DSSA), Golden Section Search Optimization (GSSO), and Northern Goshawk Optimization (NGO) were utilized. The frameworks were trained using four key input metrics: plastic limit (PL), liquid limit (LL), sleeve friction (SF), and overburden weight (OBW). The performance of the proposed frameworks was evaluated using five criteria: R², RMSE, MSE, SI, and SMAPE. Among the three hybrid frameworks developed for estimating USS, the SVR optimized with the Northern Goshawk Optimization (SVNG) algorithm outperformed the others, achieving the lowest RMSE value of 39.30 in the testing step and the highest R² value of 0.9980 in the testing step. These results demonstrate the superiority of the SVNG model in providing precise and efficient predictions of USS, surpassing conventional methods.https://aeis.bilijipub.com/article_212430_b13d77df8994556c83dff1ec5969674e.pdfundrained shear strengthsupport vector regressiondifferential squirrel search algorithmgolden section search optimizationnorthern goshawk optimization |
| spellingShingle | Rami Al-Qasimi Firas Al-Hajri The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength Advances in Engineering and Intelligence Systems undrained shear strength support vector regression differential squirrel search algorithm golden section search optimization northern goshawk optimization |
| title | The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength |
| title_full | The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength |
| title_fullStr | The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength |
| title_full_unstemmed | The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength |
| title_short | The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength |
| title_sort | implementation of a support vector regression model utilizing meta heuristic algorithms for predicting undrained shear strength |
| topic | undrained shear strength support vector regression differential squirrel search algorithm golden section search optimization northern goshawk optimization |
| url | https://aeis.bilijipub.com/article_212430_b13d77df8994556c83dff1ec5969674e.pdf |
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