Using Artificial Neural Networks Approach to Estimate Compressive Strength for Rubberized Concrete

Abstract

Artificial neural network (ANN) is a soft computing technique that has been used to predict with accuracy compressive strength known for its high variability of values. ANN is used to develop a model that can predict compressive strength of rubberized concrete where natural aggregate such as fine and coarse aggregate are replaced by crumb rubber and tire chips. The main idea in this study is to build a model using ANN with three parameters that are: water/cement ratio, Superplasticizer, granular squeleton. Furthermore, the data used in the model has been taken from various literatures and are arranged in a format of three input parameters: water/cement ratio, superplasticizer, granular squeleton that gathers fine aggregates, coarse aggregates, crumb rubber, tire chips and output parameter which is compressive strength. The performance of the model has been judged by using correlation coefficient, mean square error, mean absolute error and adopted as the comparative measures against the experimental results obtained from literature. The results indicate that artificial neural network has the ability to predict compressive strength of rubberized concrete with an acceptable degree of accuracy using new parameters.