An optimized instance based learning algorithm for estimation of compressive strength of concrete

This article proposes an optimized instance-based learning approach for prediction of the compressive strength of high performance concrete based on mix data, such as water to binder ratio, water content, super-plasticizer content, fly ash content, etc. The base algorithm used in this study is the k nearest neighbor algorithm, which is an instance-based machine leaning algorithm. Five different models were developed and analyzed to investigate the effects of the number of neighbors, the distance function and the attribute weights on the performance of the models. For each model a modified version of the differential evolution algorithm was used to find the optimal model parameters. Moreover, two different models based on generalized regression neural network and stepwise regressions were also developed. The performances of the models were evaluated using a set of high strength concrete mix data. The results of this study indicate that the optimized models outperform those derived from the standard k nearest neighbor algorithm, and that the proposed models have a better performance in comparison to generalized regression neural network, stepwise regression and modular neural networks models.