Use of Artificial Neural Network in Predicting Permeability of Dispersive Clay Treated With Lime and Pozzolan

Abstract

The treatment of a dispersive core soil can be achieved by mixing with lime and pozzolan, separately or simultaneously. On a dispersive soil treated with lime and pozzolan, experimental measurements of permeability were carried out with varying curing times and percentages of the additives. The results from these measurements were used in establishing an artificial neural network model meant to predict the permeability of more samples while being treated as carrying out laboratory measurements would be time consuming. Six parameters namely percentage passing of the 0.005 mm size (p), plasticity index (PI), maximum dry density (MDD), lime percentage (L), pozzolan percentage (pp), and curing time (t) were the inputs to the model while the output was permeability value. The prediction performances of various neural network models were evaluated using statistical performance indices such as root of the mean squared error (RMSE), the mean squared error (MSE), and the multiple coefficient of determination (R 2 ). The results show that the multilayer perceptron (MLP) neural network model with nine nodes in the hidden layer was desirable for predicting permeability of dispersive soils while being stabilized by lime and pozzolan, separately or simultaneously. For the model, R 2 =0.9895 and RMSE=3.5604×10 -8 cm/sec.