Prediction of compressive strength of concrete by neural networks

In this paper, a method to predict 28-day compressive strength of concrete by using multi-layer feed-forward neural networks (MFNNs) was proposed based on the inadequacy of present methods dealing with multiple variable and nonlinear problems. A MFNN model was built to implement the complex nonlinear relationship between the inputs (many factors that influence concrete strength) and the output (concrete strength). The neural network (NN) models give high prediction accuracy, and the research results conform to some rules of mix proportion of concrete. These demonstrate that using NNs to predict concrete strength is practical and beneficial. (C) 2000 Elsevier Science Ltd. All rights reserved.