Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly Ash
Soils may not always be suitable to fulfill their intended function. Soil improvement can be achieved by mechanical or chemical methods, especially in transportation facilities. L and FA additives are frequently used as chemical improvement additives. In this study, two natural clay samples with ext...
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2025-01-01
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| author | Gebrail Bekdaş Yaren Aydın Sinan Melih Nigdeli İnci Süt Ünver Wook-Won Kim Zong Woo Geem |
| author_facet | Gebrail Bekdaş Yaren Aydın Sinan Melih Nigdeli İnci Süt Ünver Wook-Won Kim Zong Woo Geem |
| author_sort | Gebrail Bekdaş |
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| description | Soils may not always be suitable to fulfill their intended function. Soil improvement can be achieved by mechanical or chemical methods, especially in transportation facilities. L and FA additives are frequently used as chemical improvement additives. In this study, two natural clay samples with extreme and very high plasticity were improved by using L and FA admixtures, and their properties under static and repeated loads were investigated by ML methods. Two soil samples from two different sites were analyzed. In this study, eight datasets were used. There are 14 inputs, including specific gravity (Gs), void ratio (eo), sieve analysis (+No.4, −No.200), clay size, LL, plastic limit (PL), plasticity index (PI), linear shrinkage (Ls), shrinkage limit (SL), cure day, agent, clay type, and agent percentage. The outputs are index and swelling properties (compressive, percent), compressive strengths, modulus of elasticity, and compressibility properties in soaked and non-soaked conditions. Prediction is attempted with different ML (ML) techniques. ML techniques used for regression (such as Decision Tree Regression (DTR) and K-nearest neighbors (KNN)). SHapley Additive Explanations (SHAP), the impact of inputs on outputs were observed, and it was generally found that PL and LL had the highest impact on outputs. Different performance metrics are used for evaluation. The results showed that these ML techniques can predict the static and cyclic properties of extremely high plasticity clays with high performance (R<sup>2</sup> > 0.99). These results highlight the general applicability of the used ML models on different datasets containing soil properties. |
| format | Article |
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| institution | Kabale University |
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| spelling | doaj-art-f8bdcb1191fe405487eb0df69eb3b0902025-01-24T13:26:29ZengMDPI AGBuildings2075-53092025-01-0115228810.3390/buildings15020288Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly AshGebrail Bekdaş0Yaren Aydın1Sinan Melih Nigdeli2İnci Süt Ünver3Wook-Won Kim4Zong Woo Geem5Department of Civil Engineering, Istanbul University-Cerrahpaşa, Istanbul 34320, TurkeyDepartment of Civil Engineering, Istanbul University-Cerrahpaşa, Istanbul 34320, TurkeyDepartment of Civil Engineering, Istanbul University-Cerrahpaşa, Istanbul 34320, TurkeyDepartment of Civil Engineering, Istanbul Technical University, Istanbul 34469, TurkeyDepartment of Smart City, Gachon University, Seongnam 13120, Republic of KoreaDepartment of Smart City, Gachon University, Seongnam 13120, Republic of KoreaSoils may not always be suitable to fulfill their intended function. Soil improvement can be achieved by mechanical or chemical methods, especially in transportation facilities. L and FA additives are frequently used as chemical improvement additives. In this study, two natural clay samples with extreme and very high plasticity were improved by using L and FA admixtures, and their properties under static and repeated loads were investigated by ML methods. Two soil samples from two different sites were analyzed. In this study, eight datasets were used. There are 14 inputs, including specific gravity (Gs), void ratio (eo), sieve analysis (+No.4, −No.200), clay size, LL, plastic limit (PL), plasticity index (PI), linear shrinkage (Ls), shrinkage limit (SL), cure day, agent, clay type, and agent percentage. The outputs are index and swelling properties (compressive, percent), compressive strengths, modulus of elasticity, and compressibility properties in soaked and non-soaked conditions. Prediction is attempted with different ML (ML) techniques. ML techniques used for regression (such as Decision Tree Regression (DTR) and K-nearest neighbors (KNN)). SHapley Additive Explanations (SHAP), the impact of inputs on outputs were observed, and it was generally found that PL and LL had the highest impact on outputs. Different performance metrics are used for evaluation. The results showed that these ML techniques can predict the static and cyclic properties of extremely high plasticity clays with high performance (R<sup>2</sup> > 0.99). These results highlight the general applicability of the used ML models on different datasets containing soil properties.https://www.mdpi.com/2075-5309/15/2/288MLregressionstatic and cyclic propertieshigh plasticity claylimefly ash |
| spellingShingle | Gebrail Bekdaş Yaren Aydın Sinan Melih Nigdeli İnci Süt Ünver Wook-Won Kim Zong Woo Geem Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly Ash Buildings ML regression static and cyclic properties high plasticity clay lime fly ash |
| title | Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly Ash |
| title_full | Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly Ash |
| title_fullStr | Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly Ash |
| title_full_unstemmed | Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly Ash |
| title_short | Modeling Soil Behavior with Machine Learning: Static and Cyclic Properties of High Plasticity Clays Treated with Lime and Fly Ash |
| title_sort | modeling soil behavior with machine learning static and cyclic properties of high plasticity clays treated with lime and fly ash |
| topic | ML regression static and cyclic properties high plasticity clay lime fly ash |
| url | https://www.mdpi.com/2075-5309/15/2/288 |
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