Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
Abstract The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated u...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-52825-7 |
| _version_ | 1797274685776330752 |
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| author | Gamil M. S. Abdullah Mahmood Ahmad Muhammad Babur Muhammad Usman Badshah Ramez A. Al-Mansob Yaser Gamil Muhammad Fawad |
| author_facet | Gamil M. S. Abdullah Mahmood Ahmad Muhammad Babur Muhammad Usman Badshah Ramez A. Al-Mansob Yaser Gamil Muhammad Fawad |
| author_sort | Gamil M. S. Abdullah |
| collection | DOAJ |
| description | Abstract The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS. |
| first_indexed | 2024-03-07T15:01:49Z |
| format | Article |
| id | doaj.art-d78bb0e95ed444babf9e9326590c4d73 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:01:49Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d78bb0e95ed444babf9e9326590c4d732024-03-05T19:07:35ZengNature PortfolioScientific Reports2045-23222024-01-0114111510.1038/s41598-024-52825-7Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soilGamil M. S. Abdullah0Mahmood Ahmad1Muhammad Babur2Muhammad Usman Badshah3Ramez A. Al-Mansob4Yaser Gamil5Muhammad Fawad6Department of Civil Engineering, College of Engineering, Najran UniversityInstitute of Energy Infrastructure, Universiti Tenaga NasionalDepartment of Civil Engineering, Faculty of Engineering, University of Central PunjabWater Wing, Water and Power Development Authority (WAPDA), WAPDA House PeshawarDepartment of Civil Engineering, Faculty of Engineering, International Islamic University MalaysiaDepartment of Civil, Environmental and Natural Resources Engineering, Luleå University of TechnologySilesian University of TechnologyAbstract The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.https://doi.org/10.1038/s41598-024-52825-7 |
| spellingShingle | Gamil M. S. Abdullah Mahmood Ahmad Muhammad Babur Muhammad Usman Badshah Ramez A. Al-Mansob Yaser Gamil Muhammad Fawad Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil Scientific Reports |
| title | Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil |
| title_full | Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil |
| title_fullStr | Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil |
| title_full_unstemmed | Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil |
| title_short | Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil |
| title_sort | boosting based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil |
| url | https://doi.org/10.1038/s41598-024-52825-7 |
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