Prediction of hydraulic conductivity of porous granular media by establishment of random forest algorithm

Abstract

The present study investigates the possibility of using random forest (RF) algorithm for prediction of hydraulic conductivity of coarse-sized granular material. Meanwhile, the conventional soft computing methods named artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) are considered to assess the robustness of the established approach. The characterized properties related to the particle size distribution as well as the air void content are utilized to predict the hydraulic conductivity coefficient. In addition, owing to the direct effect of the applied hydraulic head on the measured values of water flow velocity, the hydraulic gradient is also characterized as an influential parameter. The statistical comparison between the prediction capability of the RF and the results of ANN and ANFIS models corroborates the robustness of the developed procedure based on the machine learning algorithm to indirectly predict the hydraulic conductivity of the provided porous media. The results of parametric analysis mainly demonstrate that the air void content among aggregates is by far the most influential factor affecting the permeability of granular media made of coarse-sized aggregate.