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 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 (R2 > 0.99). These results highlight the general applicability of the used ML models on different datasets containing soil properties.

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