Experimental and numerical prediction of California bearing ratio of expansive soil stabilized by bagasse ash and geotextile reinforcement

The target of this paper is to predict the CBR value by correlating the optimum moisture content, maximum dry density, Plasticity Index, proportion of sugarcane bagasse ash and also the variety of Geotextile layers. The linear relationships between the higher than mentioned properties and CBR worth exploitation Multiple rectilinear regression Analysis resulted during a sturdy correlation between the parameters. ANN model is developed exploitation ANN tool of MATLAB R2014a software. A multilayer perceptron network with feed forward back propagation is used to model variable the number of hidden layers. The graph aforethought between the predicted and experimental values shows that everyone the values are getting ready to equality line, indicating foreseen values are regarding the discovered values.

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