Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach
This research aimed to investigate the effectiveness of Polyethylene-Terephthalate (PET) as a reinforcement material for sandy soils in enhancing the shear strength. To achieve this, different concentrations of PET were tested, and 118 sets of data were collected. Parameters such as relative density...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Springer Nature
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/95502 |
| _version_ | 1848766019024191488 |
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| author | Samaei, Masoud Alinejad Omran, Morteza Keramati, Mohsen Naderi, Reza Shirani Faradonbeh, Roohollah |
| author_facet | Samaei, Masoud Alinejad Omran, Morteza Keramati, Mohsen Naderi, Reza Shirani Faradonbeh, Roohollah |
| author_sort | Samaei, Masoud |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This research aimed to investigate the effectiveness of Polyethylene-Terephthalate (PET) as a reinforcement material for sandy soils in enhancing the shear strength. To achieve this, different concentrations of PET were tested, and 118 sets of data were collected. Parameters such as relative density, normal stress in direct shear strength test, and types of PET elements (1 × 1, 1 × 5, and fiber) were also recorded. Subsequently, four decision tree-oriented machine learning (ML) methods—decision tree (DT), random forest (RF), AdaBoost, and XGBoost—were applied to construct models capable of forecasting enhancements in shear strength. The evaluation of these models' effectiveness was conducted using four established statistical metrics: R2, RMSE, VAF, and A-10. The results showed that AdaBoost results in the highest prediction accuracy among other algorithms, representing the high modelling performance of the algorithm in dealing with complex nonlinear problems. The conducted sensitivity analysis also revealed that relative density is the most crucial parameter for all the algorithms in predicting the output, followed by PET percentage and normal stress. Furthermore, to make the developed model in this study more practical and easy to use, a Graphical User Interface (GUI) was created, enabling the engineers and researchers to perform the analysis straightforwardly. |
| first_indexed | 2025-11-14T11:44:29Z |
| format | Journal Article |
| id | curtin-20.500.11937-95502 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:44:29Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-955022024-08-30T02:26:19Z Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach Samaei, Masoud Alinejad Omran, Morteza Keramati, Mohsen Naderi, Reza Shirani Faradonbeh, Roohollah This research aimed to investigate the effectiveness of Polyethylene-Terephthalate (PET) as a reinforcement material for sandy soils in enhancing the shear strength. To achieve this, different concentrations of PET were tested, and 118 sets of data were collected. Parameters such as relative density, normal stress in direct shear strength test, and types of PET elements (1 × 1, 1 × 5, and fiber) were also recorded. Subsequently, four decision tree-oriented machine learning (ML) methods—decision tree (DT), random forest (RF), AdaBoost, and XGBoost—were applied to construct models capable of forecasting enhancements in shear strength. The evaluation of these models' effectiveness was conducted using four established statistical metrics: R2, RMSE, VAF, and A-10. The results showed that AdaBoost results in the highest prediction accuracy among other algorithms, representing the high modelling performance of the algorithm in dealing with complex nonlinear problems. The conducted sensitivity analysis also revealed that relative density is the most crucial parameter for all the algorithms in predicting the output, followed by PET percentage and normal stress. Furthermore, to make the developed model in this study more practical and easy to use, a Graphical User Interface (GUI) was created, enabling the engineers and researchers to perform the analysis straightforwardly. 2024 Journal Article http://hdl.handle.net/20.500.11937/95502 10.1007/s12145-024-01398-0 Springer Nature restricted |
| spellingShingle | Samaei, Masoud Alinejad Omran, Morteza Keramati, Mohsen Naderi, Reza Shirani Faradonbeh, Roohollah Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach |
| title | Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach |
| title_full | Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach |
| title_fullStr | Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach |
| title_full_unstemmed | Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach |
| title_short | Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach |
| title_sort | assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an ai-based approach |
| url | http://hdl.handle.net/20.500.11937/95502 |