Comprehensive analysis of machine learning models for predicting concrete compressive strength

Abstract

Predicting the compressive strength of concrete provides several benefits including insights into its durability, potential lifespan, and its suitability for specific construction projects. Recent advances in machine learning algorithms have prompted their application to  compressive strength modeling and prediction. In this paper, we carry out a comprehensive comparative analysis of the existing machine learning algorithms. Unlike, other current studies in this domain, we consider the entire spectrum of algorithms ranging from basic linear models to advanced meta-learning methods. The results show that there exists a trade-off between speed and accuracy of the models. We find that while ensemble models such as stacking and gradient boost provide superior accuracy, they are significantly slower than linear models.