Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
MDPI AG
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/92662 |
| _version_ | 1848765652087603200 |
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| author | Daghistani, Firas Baghbani, Abolfazl Abuel-Naga, Hossam Shirani Faradonbeh, Roohollah |
| author_facet | Daghistani, Firas Baghbani, Abolfazl Abuel-Naga, Hossam Shirani Faradonbeh, Roohollah |
| author_sort | Daghistani, Firas |
| building | Curtin Institutional Repository |
| collection | Online Access |
| first_indexed | 2025-11-14T11:38:39Z |
| format | Journal Article |
| id | curtin-20.500.11937-92662 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:38:39Z |
| publishDate | 2023 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-926622023-07-19T05:09:45Z Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning Daghistani, Firas Baghbani, Abolfazl Abuel-Naga, Hossam Shirani Faradonbeh, Roohollah 2023 Journal Article http://hdl.handle.net/20.500.11937/92662 10.3390/geosciences13070197 This study aimed to examine the shear strength characteristics of sand–granular rubber mixtures in direct shear tests. Two different sizes of rubber and one of sand were used in the experiment, with the sand being mixed with various percentages of rubber (0%, 10%, 20%, 30%, and 50%). The mixtures were prepared at three different densities (loose, slightly dense, and dense), and shear stress was tested at four normal stresses (30, 55, 105, and 200 kPa). The results of 80 direct shear tests were used to calculate the peak and residual internal friction angles of the mixtures, and it was found that the normal stress had a significant effect on the internal friction angle, with an increase in normal stress leading to a decrease in the internal friction angle. These results indicated that the Mohr–Coulomb theory, which applies to rigid particles only, is not applicable in sand–rubber mixtures, where stiff particles (sand) and soft particles (rubber) are mixed. The shear strength of the mixtures was also influenced by multiple factors, including particle morphology (size ratio, shape, and gradation), mixture density, and normal stress. For the first time in the literature, genetic programming, classification and regression random forests, and multiple linear regression were used to predict the peak and residual internal friction angles. The genetic programming resulted in the creation of two new equations based on mixture unit weight, normal stress, and rubber content. Both artificial intelligence models were found to be capable of accurately predicting the peak and residual internal friction angles of sand–rubber mixtures. http://creativecommons.org/licenses/by/4.0/ MDPI AG fulltext |
| spellingShingle | Daghistani, Firas Baghbani, Abolfazl Abuel-Naga, Hossam Shirani Faradonbeh, Roohollah Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning |
| title | Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning |
| title_full | Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning |
| title_fullStr | Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning |
| title_full_unstemmed | Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning |
| title_short | Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning |
| title_sort | internal friction angle of cohesionless binary mixture sand–granular rubber using experimental study and machine learning |
| url | http://hdl.handle.net/20.500.11937/92662 |