Modelling the Effects of Nanomaterial Addition on the Permeability of the Compacted Clay Soil Using Machine Learning-Based Flow Resistance Analysis

Impermeable base layers that are made of materials with low permeability, such as clay soil, necessary to prevent leachate in landfills from harming the environment. However, over time, permeability soil changes. Therefore, reduce and minimize risk, permeability-related characteristics must be improved. Thus, this study aims serve purpose by experimentally investigating effects nanomaterial addition (aluminum oxide, iron oxide) into kaolin samples. The obtained samples prepared applying standard compaction, sample is investigated passing reactors, which these placed. Flow Resistance (FR) analysis conducted results show Al additives more successful than Fe additive reducing permeability. Besides, concentration values some polluting parameters (Chemical Oxygen Demand (COD), Total Kjeldahl Nitrogen (TKN), Phosphorus (TP)) at inlet outlet reactors analyzed. Three different models (Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), Support Vector Machine (SVM)) applied data experimental study. have shown produce high FR regression similarity rates (>75%), TKN, TP, COD features highly correlated value (>60%) most method found SVM model.