Prediction of Compressive Strength of Concrete: Machine Learning Approaches

Abstract

Abrams’ law is commonly used to predict the compressive strength of concrete with respect to the water content of the mix, but it is largely inaccurate. High-performance concrete, with its complex additional ingredients, makes the prediction more difficult. The goal of the paper is to find the most accurate model for prediction of the compressive strength of a given concrete mix using machine learning (ML). First, the various ML models are explained along with their working principles. Second, the evaluation methods used for the error analysis in the study are discussed. Third, the findings of the study are displayed and inferences are drawn from them. It is found that the 2-nearest-neighbour performs the best with an error of 8.5% and a standard deviation of 1.55.