Modeling the compressive strength of molasses–cement sand system using design of experiments and back propagation neural network

Molasses, an eco-friendly and relatively cheap binder may be used as a substitute for chemical binders. For commercial exploitation of the molasses–cement sand system it is essential to generate models for predicting the properties of the sand mix from the composition. Central composite design is used to develop regression equations for predicting compressive strength of the sand mix when molasses is varied between 5.5% and 7.5% and cement between 2% and 4%. Though central composite design is an effective tool for studying the complex effects of number of independent variables on response factor it has quite a few limitations. Back propagation neural network is not only capable of modeling highly non-linear relationship using dispersed data in the solution domain but has a few advantages over the central composite design. But one of the major drawbacks of this network is that no theoretical basis exists to determine the number of hidden layers and number of neurons therein. Different configurations of BPNN have great effects on the predicted results. Back propagation neural networks of different configurations are trained. Results obtained form these networks are analyzed and compared with those obtained form regression equations and experiments. Guidelines for selecting the effective configuration of back propagation networks are proposed.