Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In or...
| المؤلفون الرئيسيون: | , |
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| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
MDPI AG
2021-01-01
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| سلاسل: | Applied Sciences |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.mdpi.com/2076-3417/11/2/536 |
| _version_ | 1827603617138343936 |
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| author | Mohammed Amin Benbouras Alexandru-Ionut Petrisor |
| author_facet | Mohammed Amin Benbouras Alexandru-Ionut Petrisor |
| author_sort | Mohammed Amin Benbouras |
| collection | DOAJ |
| description | Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modeling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are utilized to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modeling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of <i>FS-RF</i> model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies. |
| first_indexed | 2024-03-09T05:43:16Z |
| format | Article |
| id | doaj.art-7e5fd7c696004364a0115b9be9db7af8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:43:16Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7e5fd7c696004364a0115b9be9db7af82023-12-03T12:23:41ZengMDPI AGApplied Sciences2076-34172021-01-0111253610.3390/app11020536Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive SoilsMohammed Amin Benbouras0Alexandru-Ionut Petrisor1École Normale Supérieure d’Enseignement Technologique de Skikda (ENSET), Skikda 21000, AlgeriaDoctoral School of Urban Planning, Ion Mincu University of Architecture and Urbanism, 010014 Bucharest, RomaniaSeveral attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modeling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are utilized to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modeling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of <i>FS-RF</i> model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies.https://www.mdpi.com/2076-3417/11/2/536swelling indexmachine learningoedometer testsphysical soil parametersneural networkRandom Forest |
| spellingShingle | Mohammed Amin Benbouras Alexandru-Ionut Petrisor Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils Applied Sciences swelling index machine learning oedometer tests physical soil parameters neural network Random Forest |
| title | Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils |
| title_full | Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils |
| title_fullStr | Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils |
| title_full_unstemmed | Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils |
| title_short | Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils |
| title_sort | prediction of swelling index using advanced machine learning techniques for cohesive soils |
| topic | swelling index machine learning oedometer tests physical soil parameters neural network Random Forest |
| url | https://www.mdpi.com/2076-3417/11/2/536 |
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