Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations
Pile foundations are vastly utilized in construction projects where their capacities (pile bearing capacity, PBC) should be determined in different stages of construction. A highly reliable and accurate prediction model can lead to many advantages, such as reducing the construction cost, shortening...
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| Format: | Article |
| Language: | English |
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Pouyan Press
2023-07-01
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| Series: | Journal of Soft Computing in Civil Engineering |
| Subjects: | |
| Online Access: | https://www.jsoftcivil.com/article_169219_7f5241581497d32a89dd2bc7cf5b956b.pdf |
| _version_ | 1797797748069629952 |
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| author | Danial Jahed Armaghani Yong Yi Ming Ahmedh Salih Mohammed Ehsan Momeni Harnedi Maizir |
| author_facet | Danial Jahed Armaghani Yong Yi Ming Ahmedh Salih Mohammed Ehsan Momeni Harnedi Maizir |
| author_sort | Danial Jahed Armaghani |
| collection | DOAJ |
| description | Pile foundations are vastly utilized in construction projects where their capacities (pile bearing capacity, PBC) should be determined in different stages of construction. A highly reliable and accurate prediction model can lead to many advantages, such as reducing the construction cost, shortening the construction timeline, and providing safety construction. Hence, the aim of this study is the developments of statistical and artificial intelligence (AI) models for predicting bearing capacities of 141 piles. At the preliminary of the study, features or inputs of this study to predict PBC were selected trough simple regression analysis. Then, this study presents different kernels of support vector machine (SVM) technique, i.e., the dot, the radial basis function (RBF), the polynomial, the neural, and the ANOVA to predict the PBC. The aforementioned models were evaluated by several performance indices and their results were compared using a simple ranking system. The results showed that the SVM-RBF model is able to achieve the highest coefficient of determination, R2 values which are 0.967 and 0.993 for training and testing stages, respectively. It is important to mention that a multiple regression model was also employed to predict PBC values. The other SVM kernels were provided a high degree of accuracy for estimating PBC, however, the SVM-RBF model is recommended to be used as a powerful, highly reliable, and simple solution for PBC prediction. |
| first_indexed | 2024-03-13T03:53:03Z |
| format | Article |
| id | doaj.art-99bd342e17df4a6091fb399e536f17c9 |
| institution | Directory Open Access Journal |
| issn | 2588-2872 |
| language | English |
| last_indexed | 2024-03-13T03:53:03Z |
| publishDate | 2023-07-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj.art-99bd342e17df4a6091fb399e536f17c92023-06-22T09:24:30ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722023-07-017311112810.22115/scce.2023.356959.1510169219Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep FoundationsDanial Jahed Armaghani0Yong Yi Ming1Ahmedh Salih Mohammed2Ehsan Momeni3Harnedi Maizir4Centre of Tropical Geoengineering (GEOTROPIK), Institute of Smart Infrastructure and Innovative Engineering (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, MalaysiaCivil Engineering Department, College of Engineering, University of Sulaimani, Kurdistan Region, IraqDepartment of Civil Engineering, Faculty of Engineering, Lorestan University, Khorramabad 6815144316, IranDepartment of Civil Engineering, Sekolah Tinggi Teknologi Pekanbaru, IndonesiaPile foundations are vastly utilized in construction projects where their capacities (pile bearing capacity, PBC) should be determined in different stages of construction. A highly reliable and accurate prediction model can lead to many advantages, such as reducing the construction cost, shortening the construction timeline, and providing safety construction. Hence, the aim of this study is the developments of statistical and artificial intelligence (AI) models for predicting bearing capacities of 141 piles. At the preliminary of the study, features or inputs of this study to predict PBC were selected trough simple regression analysis. Then, this study presents different kernels of support vector machine (SVM) technique, i.e., the dot, the radial basis function (RBF), the polynomial, the neural, and the ANOVA to predict the PBC. The aforementioned models were evaluated by several performance indices and their results were compared using a simple ranking system. The results showed that the SVM-RBF model is able to achieve the highest coefficient of determination, R2 values which are 0.967 and 0.993 for training and testing stages, respectively. It is important to mention that a multiple regression model was also employed to predict PBC values. The other SVM kernels were provided a high degree of accuracy for estimating PBC, however, the SVM-RBF model is recommended to be used as a powerful, highly reliable, and simple solution for PBC prediction.https://www.jsoftcivil.com/article_169219_7f5241581497d32a89dd2bc7cf5b956b.pdfdeep foundation capacitysupport vector machinekernelslinear and multiple regression |
| spellingShingle | Danial Jahed Armaghani Yong Yi Ming Ahmedh Salih Mohammed Ehsan Momeni Harnedi Maizir Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations Journal of Soft Computing in Civil Engineering deep foundation capacity support vector machine kernels linear and multiple regression |
| title | Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations |
| title_full | Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations |
| title_fullStr | Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations |
| title_full_unstemmed | Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations |
| title_short | Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations |
| title_sort | effect of svm kernel functions on bearing capacity assessment of deep foundations |
| topic | deep foundation capacity support vector machine kernels linear and multiple regression |
| url | https://www.jsoftcivil.com/article_169219_7f5241581497d32a89dd2bc7cf5b956b.pdf |
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