Predicting compressive strength of SCC mixtures using artif icial neural network

Abstract

Over the last few years, the use of artificial neural networks (ANNs) has increased in many areas of engineering. In particular it is increasingly being used in concrete engineering problems. Since accurate estimation of compressive strength of self-compacting concrete (SCC) is an important issue in concrete engineering this paper describes the development of ANN models based on laboratory SCC mixes. The multilayer feed-forward type network models were trained using the back-propagation method with a momentum factor. The data obtained from the mix design exercises were employed to develop and test the performance of the models. A new concept of using more than one error statistic resulted in efficiently training the models and improving its generalization capability.