Artificial neural networks prediction of compaction characteristics of black cotton soil stabilized with cement kiln dust

Abstract

Artificial neural networks (ANNs) that has been successfully applied to structural and most other disciplines of civil engineering is yet to be extended to soil stabilization aspect of geotechnical engineering. As such, this study aimed at applying the ANNs as a soft computing approach that were trained with the feed forward back-propagation algorithm, for the simulation of optimum moisture content (OMC) and maximum dry density (MDD) of cement kiln dust-stabilized black cotton soil. Ten input and two output data set were used for the ANN model development. The mean squared error (MSE) and R-value were used as yardstick and criterions for acceptability of performance. In the neural network development, NN 10-5-1 and NN 10-7-1 respectively for OMC and MDD that gave the lowest MSE value and the highest R-value were used in the hidden layer of the networks architecture and performed satisfactorily. For the normalized data used in training, testing and validating the neural network, the performance of the simulated network was satisfactory having R values of 0.983 and 0.9884 for the OMC and MDD, respectively. These values met the minimum criteria of 0.8 conventionally recommended for strong correlation condition. All the obtained simulation results are satisfactory and a strong correlation was observed between the experimental OMC and MDD values as obtained by laboratory tests and the predicted values using ANN.