Impact of waste foundry sand on drainage behavior of sandy soil: an experimental and machine learning study

The study of drainage behavior is essential for using waste material in geotechnical applications. In this study, sandy soil was replaced with waste foundry sand (WFS) at an incremental interval of 20% by weight. Permeability (k) for each mix was acquired at three relative densities (R_D), i.e., 65%, 75% and 85%, by using the constant head method. Then the results were further processed with machine learning (ML) models to validate the experimental data. The experimental study demonstrated that k would decrease with the increase in relative density and WFS content. A rise in R_D from 65% to 85% resulted in a substantial reduction of up to 140% in the value of k. Moreover, the complete replacement of sand with WFS reduced the value of k by 36%, 51% and 57% for R_D of 65%, 75% and 85%, respectively. The total dataset of 90 observations was divided at a ratio of 63/13/15 into training/validation/testing datasets for ML-AI modeling. Input variables include percentage of sand (BS), replacement with WFS, total head (H), time interval (t) and outflow (Q); and k is the output variable. The methods of artificial neural network (ANN), random forest (RF), decision tree (DT) and multi-linear regression (MLR) are used for k prediction. It is found that the random forest approach performed outstandingly in these methods, with an R² value of 0.9955. The performance of all the proposed methods was compared and verified with Taylor's diagram. Sensitivity analysis showed that Q and R_D were the most influential parameters for predicting k values.