Predicting the Compressive Strength of Concrete Using an RBF-ANN Model

• Published in: Applied sciences Vol. 11; no. 14; p. 6382
• Main Author: Wu, Nan-Jing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Algorithms; artificial neural networks; Compressive strength; compressive strength of concrete; Mathematical functions; mix proportioning of concrete; Neural networks; Neurons; Parameters; Partial differential equations; prediction model; Proportioning (mixing); Radial basis function; radial basis functions; Stochasticity
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11146382

In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in the author’s previous work. The stochastic gradient approach presented in the textbook is employed for determining the centers of RBFs and their shape parameters. With an extremely large number of training iterations and just a few RBFs in the ANN, all the RBF-ANNs have converged to the solutions of global minimum error. So, the only consideration of whether the ANN can work in practical uses is just the issue of over-fitting. The ANN with only three RBFs is finally chosen. The results of verification imply that the present RBF-ANN model outperforms the BP-ANN model in the author’s previous work. The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. With these numbers and using the formulae expressed in this article, anyone can predict the 28-day compressive strength of concrete according to the concrete mix proportioning on his/her own.