Prediction of Compressive Strength of SCC-Containing Metakaolin and Rice Husk Ash Using Machine Learning Algorithms

Abstract

Self-compacting concrete (SCC) enhanced by the addition of a supplementary admixture of metakaolin and/or modifying agent rice husk ash has been the object of several types of research due to its display of different properties than normal vibrated concrete. This study aimed at utilizing machine learning approaches, viz, Support Vector Machine, Multilayer Perceptron, Gaussian Processes Regression, random tree, M5P tree, and Random Forest to develop models using seven input parameters, viz, cement (kg/m3), water (kg/m3), fine aggregates (kg/m3), coarse aggregates (kg/m3), superplasticizers (kg/m3), metakaolin (kg/m3), and rice husk ash (kg/m3) and one output parameter being the 28-day compressive strength (MPa) of SCC mix ratios derived from reliable literature. The dataset consisted of 159 mix ratios with their 28-day compressive strength. The results obtained for the dataset are subject to two different methodologies: the supplied test data method and the cross-validation method. The results obtained are compared and allowed users to make a reasonable prediction of compressive strength for mix ratios with the aid of models developed.