APPLICATIONS OF NEURAL NETWORK AND NEURO-FUZZY NETWORK TO ESTIMATE THE PARAMETERS OF SELF-COMPACTING CONCRETE

Abstract

   The paper presents the new application of two classical nonlinear estimators, which are the multi layer perceptron and the neuro-fuzzy networks, to approximate the workability parameters of fresh self-compacting concrete based on the amount of input ingredients like cement, fly ash, water, additives or admixtures. The estimation of workability parameters is much needed to determine the quality of the fresh self-compacting concrete before starting the production. A total of 360 real field tests of 30 types of self-compacting concrete were conducted and seven basic parameters were measured for each test. These samples will form the training and testing data sets for the nonlinear models. The numerical results showed that the MLP network could estimate the workability parameters with relative errors less than 3.6% and the TSK could estimate with te relative errors less than 2.7%. These results proved the ability to create high accuracy approximation models of the proposed solutions, where the neuro-fuzzy model would show a little better performance than the multilayer perceptron. Both of the models required only relatively simple structures, making them more promising to be used in practical applications.