Prediction of Concrete Strength Using Floating Centroids Method

Concrete is viewed as the most important cement-based composite material in the field of civil engineering. Its strength is considered the most important among its mechanical properties. Although the value of strength can be directly forecasted, the estimation of strength grade remains particularly important because concrete mortar is non-uniform, and practical preparation and curing cannot be fully simulated under laboratory conditions. In this paper, concrete strength grade was predicted by using the floating centroids method neural network classifier, which removes the fixed-centroid constraint and increases the possibility of finding an optimal neural network. Experimental results show that concrete strength prediction performance is improved by employing the floating centroids method.