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01-06-2015 | Technical Note | Issue 2/2015

International Journal of Geosynthetics and Ground Engineering 2/2015

Prediction of Deviator Stress of Sand Reinforced with Waste Plastic Strips Using Neural Network

Journal:
International Journal of Geosynthetics and Ground Engineering > Issue 2/2015
Authors:
Rakesh Kumar Dutta, Kamlesh Dutta, S. Jeevanandham

Abstract

The paper presents a study conducted on sand-waste plastic strip mixture for carrying out consolidated drained triaxial compression tests and to use the experimental data in training, testing, and prediction phases of neural network-based soil models. The input variables in the developed neural network models were strip content, tensile strength of strip, thickness of the strip, elongation at failure of the strip, aspect ratio, dry unit weight of the composite specimen, confining pressure and strain at failure of the composite specimen and the output was the deviator stress. These variables were considered to construct 8-6-1 topology of neural network in the prediction of the deviator stress. Further, using the mean squared error, root mean squared error, mean absolute error, mean absolute percentage error, correlation coefficient (r) and coefficient of determination (R 2) for the training and testing data, the predictability of neural networks was analysed using various activation functions. The neural network model obtained had an acceptable accuracy. Sensitivity analysis revealed that the contribution of the input variables such as strip thickness, tensile strength of the strip and dry unit weight does not have much impact on the output deviator stress. After the sensitivity analysis, neural network structure was revised. The revised model having 5-4-1 topology gives a better prediction of the output deviator stress than the previous model with 8-6-1 topology. Further, the revised neural network model having 5-4-1 topology is superior to the one obtained using multiple regression analysis in predicting the output deviator stress. Finally a model equation is presented based on trained weights in the revised neural network.

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