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Advanced Probabilistic Neural Network for the Prediction of Concrete Strength

Advanced Probabilistic Neural Network for the Prediction of Concrete Strength Doo Kie Kim1, Seong Kyu Chang1 and Sang Kil Chang1 Summary Accurate and realistic strength estimation before the placement of concrete is highly desirable. In this study, the advanced probabilistic neural network (APNN) was proposed to reflect the global probability density function by summing the heterogeneous local probability density function automatically determined in the individual standard deviation of variables. Currently, the estimation of the compressive strength of concrete is performed by a probabilistic neural network (PNN) on the basis of concrete mix proportions, and the PNN is improved by the iteration method. However, an empirical method has been incorporated to specify the smoothing parameter in the PNN technique, causing significant uncertainty in the estimation results. In addition, the probability density function (PDF) is the sum of homogeneous multivariate Gaussian distribution because only one global smoothing parameter is used. The APNN was applied to predict the compressive strength of concrete using actual test data from two concrete companies, and the estimated results of APNN were compared with those of PNN. APNN showed better results than PNN in predicting the compressive strength of concrete and provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.