Measurement and Prediction of Effect Slenderness Ratios and Aggregate to the Compressive Strength of Concrete by the Core-Drilling Method in Tandem with Machine Learning

Abstract

The slenderness ratio, length to diameter, of the cylindrical concrete samples of the slab block by the core-drilling method is believed to affect the compressive strength other than the aggregates in the concrete. In this study, the relationship between the compressive strength with mixing and slenderness ratio of cylindrical concrete specimens was investigated by statistics. Further, the discrimination model for mixing cylindrical concrete specimens has been developed by using machine learning algorithms, including support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and random forest (RF). A total of 180 cylindrical concrete specimens have been measured for compressive strength using UTM. The sample consisted of a mixture of type-A and type-B with a slenderness ratio of 2.48, 2.72, and 3.28, respectively. Samples were obtained by the core-drilling method from slab block concrete. The ANOVA tests showed that the aggregate and slenderness ratio caused a significant difference in the compressive strength of the concrete (p<0.05). This indicates that the type of aggregate mixture in concrete and the slenderness ratio of cylindrical concrete specimens significantly affect the compressive strength of the concrete. The model for discrimination of mixing cylindrical concrete specimens using machine learning algorithms can be used with satisfactory results. LDA is a machine learning algorithm that can show stability in the training and testing stages with accuracy reaching 78% and inconsistency of less than 2.63% (the smallest compared to others). The descending order of machine learning algorithms based on their consistency is LDA > RF > SVM > k-NN. Subsequently, this model can discriminate the aggregate mixture on cylindrical concrete specimens obtained from the core-drilling method.