Prediction of Hydraulic Conductivity of Soil Bentonite Mixture Using Hybrid-ANN Approach

Abstract

Due to its low hydraulic conductivity compacted soil-bentonite mixture is widely used as a barrier material at waste disposal site. The experimental determination of hydraulic conductivity of soil-bentonite mixture, which depends on the various physical and chemical and mineralogical factors, requires expensive and time consuming setup. Thus, a hybrid neural network model (combining genetic algorithm with neural network) is presented here as a complementary tool to model hydraulic conductivity of soil-bentonite mixture. The prediction capability of the model has been found to be satisfactory. The developed model yielded correlation coefficients of 0.98 and 0.97 for training and testing data sets, respectively. The proposed model was compared with conventional neural network models by using different statistical indicators such as Nash-Sutcliffe model efficiency and discrepancy ratio with standard deviation. It was found that the predictions obtained from developed model agreed well with experimental observations. Identification of important parameters and ranking their order of influence on hydraulic conductivity has been discussed by using input significance test.

Keywords: bentonite, genetic algorithm, hydraulic conductivity, liquid limit, neural network