Prediction of the Concrete Slump with Artificial Neural Network Model and the Multivariable Linear Regression Method

Workability is the important property of a fresh concrete. There are many methods to measure the workability of concrete. One of the most common and frequently used method is the slump test. In order to achieve the concrete with a desired workability, different concrete mixtures must be made and the slump test be done. To save time, money and material, the prediction methods are used to predict the slump on the basis of the results obtain from a certain number of concrete mixtures. In this study, Multivariable Linear Regression (MLR) method and the Artificial Neural Network (ANN) model, as one of soft computation algorithms, are utilized to evaluate slump prediction and the results in terms of applicability, accuracy and efficiency are compared. The proposed ANN model is the multilayer perceptron with back-propagation learning algorithm. The results showed that the predicted values of both methods are desirable and acceptable. The correlation coefficient, mean square error and the mean absolute error in the ANN model are respectively 0/9853, 0/485 and 0/547. These values in the multivariable linear regression method are respectively 0/8717, 1/7731 and 0/9136. In the multivariable linear regression method, a linear relationship between independent and dependent variables is determined. But, in the most cases and reality, this relationship is not extremely linear, so the predicted values in this method may have big errors and can be neglected. The ANN model, predicts the output by learning the true relationship between inputs and output parameters.