Prediction of permeability of amended soil using ensembled artificial intelligence models

Abstract

Soil permeability is a critical parameter that dictates the movement of water through soil, and it impacts processes such as seepage, erosion, slope stability, foundation design, groundwater contamination, and various engineering applications. This study investigates the permeability of soil amended with waste foundry sand (WFS) at a replacement level of 10%. Permeability measurements are conducted for three distinct relative densities, spanning from 65% to 85%. The dataset compiled from these measurements is employed to develop ensemble artificial intelligence (AI) models. Specifically, four regressor AI models are considered: Nearest Neighbor (NNR), Decision Tree (DTR), Random Forest (RFR) and Support Vector Machine (SVR). These models are enhanced with four distinct base learners: Gradient Boosting (GB), Stacking Regressor (SR), AdaBoost Regressor (ADR), and XGBoost (XGB). The input parameters include fraction of base sand (BS), fraction of waste foundry sand (WFS), relative density (RD), duration of flow (T), quantity of flow (Q) and permeability (k), totalling 165 data points. Through comparative analysis, the Gradient Boost with Decision Tree (GB-DTR) model is found to be best-performed model, with R² = 0.9919. Sensitivity analysis reveals that Q is the most influential input parameter in predicting soil permeability.