Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble models
This study introduces an innovative method for soil stabilization by integrating geopolymer binders with a non-iterative hybrid ensemble modeling framework. Using 270 samples, key parameters such as molarity, silt content, and chemical ratios (Si/Al, Na/Al) were analyzed to predict the unconfined co...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025022054 |
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| author | Ishwor Thapa Sufyan Ghani Nishant Kumar Megha Gupta Sunil Saharan Prabhu Paramasivam Abinet Gosaye Ayanie |
| author_facet | Ishwor Thapa Sufyan Ghani Nishant Kumar Megha Gupta Sunil Saharan Prabhu Paramasivam Abinet Gosaye Ayanie |
| author_sort | Ishwor Thapa |
| collection | DOAJ |
| description | This study introduces an innovative method for soil stabilization by integrating geopolymer binders with a non-iterative hybrid ensemble modeling framework. Using 270 samples, key parameters such as molarity, silt content, and chemical ratios (Si/Al, Na/Al) were analyzed to predict the unconfined compressive strength (UCS) of geopolymer-stabilized clay. Five machine learning models Random Forest, Support Vector Regression, Extreme Learning Machine, Artificial Neural Networks, and Multivariate Adaptive Regression Splines were developed and combined in a unique hybrid ensemble. This approach eliminates the need for iterative optimization, ensuring computational efficiency. Among the hybrid models, Hybrid Random Forest (HRF) and Hybrid Artificial Neural Network (HANN) delivered superior performance, with HRF achieving an R² of 0.997 (training) and 0.983 (testing), and an RMSE of 0.784 MPa. Additionally, HRF exhibited an Index of Agreement (IOA) of 0.996, indicating exceptional alignment with actual UCS values. A20-index scores for HRF and HANN were 0.982 and 0.973, respectively, highlighting their robustness across diverse conditions. The study’s non-iterative hybrid ensemble approach offers flexible and accurate UCS predictions while reducing computational demands. This research emphasizes the potential of geopolymer materials and advanced machine learning models to advance sustainable and efficient soil stabilization solutions in geotechnical engineering. |
| format | Article |
| id | doaj-art-6c99754daf1b4da39f64d13b3b59404d |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-6c99754daf1b4da39f64d13b3b59404d2025-08-20T03:50:21ZengElsevierResults in Engineering2590-12302025-09-012710613310.1016/j.rineng.2025.106133Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble modelsIshwor Thapa0Sufyan Ghani1Nishant Kumar2Megha Gupta3Sunil Saharan4Prabhu Paramasivam5Abinet Gosaye Ayanie6Department of Civil Engineering, Sharda University, Greater Noida, IndiaEngineer - Tailings (Mine Specialist Team) GCC, WSP IndiaDepartment of Civil Engineering, Sharda University, Greater Noida, IndiaDepartment of Civil Engineering, Sharda University, Greater Noida, IndiaDepartment of Civil Engineering, Sharda University, Greater Noida, IndiaDepartment of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, 602105, India; Corresponding authors.Department of Mechanical Engineering, Adama Science and Technology University, Adama, 2552, Ethiopia; Corresponding authors.This study introduces an innovative method for soil stabilization by integrating geopolymer binders with a non-iterative hybrid ensemble modeling framework. Using 270 samples, key parameters such as molarity, silt content, and chemical ratios (Si/Al, Na/Al) were analyzed to predict the unconfined compressive strength (UCS) of geopolymer-stabilized clay. Five machine learning models Random Forest, Support Vector Regression, Extreme Learning Machine, Artificial Neural Networks, and Multivariate Adaptive Regression Splines were developed and combined in a unique hybrid ensemble. This approach eliminates the need for iterative optimization, ensuring computational efficiency. Among the hybrid models, Hybrid Random Forest (HRF) and Hybrid Artificial Neural Network (HANN) delivered superior performance, with HRF achieving an R² of 0.997 (training) and 0.983 (testing), and an RMSE of 0.784 MPa. Additionally, HRF exhibited an Index of Agreement (IOA) of 0.996, indicating exceptional alignment with actual UCS values. A20-index scores for HRF and HANN were 0.982 and 0.973, respectively, highlighting their robustness across diverse conditions. The study’s non-iterative hybrid ensemble approach offers flexible and accurate UCS predictions while reducing computational demands. This research emphasizes the potential of geopolymer materials and advanced machine learning models to advance sustainable and efficient soil stabilization solutions in geotechnical engineering.http://www.sciencedirect.com/science/article/pii/S2590123025022054Sustainable unconfined compression strengthMachine learning geo-polymer clay |
| spellingShingle | Ishwor Thapa Sufyan Ghani Nishant Kumar Megha Gupta Sunil Saharan Prabhu Paramasivam Abinet Gosaye Ayanie Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble models Results in Engineering Sustainable unconfined compression strength Machine learning geo-polymer clay |
| title | Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble models |
| title_full | Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble models |
| title_fullStr | Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble models |
| title_full_unstemmed | Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble models |
| title_short | Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble models |
| title_sort | sustainable approach of strength measurement for soil s stabilized with geo polymer with hybrid ensemble models |
| topic | Sustainable unconfined compression strength Machine learning geo-polymer clay |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025022054 |
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