Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: a review

Abstract

Concrete is one of the most common construction materials used all over the world. Estimating the strength properties of concrete traditionally demands extensive laboratory experimentation. However, researchers have increasingly turned to predictive models to streamline this process. This review focuses on predicting the compressive strength of self-compacting concrete using artificial intelligence (AI) techniques. Self-compacting concrete represents an advanced construction material particularly suited for scenarios where traditional vibrational methods face limitations due to intricate formwork or reinforcement complexities. This review evaluates various AI techniques through a comparative performance analysis. The findings highlight that employing Deep Neural Network models with multiple hidden layers significantly enhances predictive accuracy. Specifically, artificial neural network (ANN) models exhibit robustness, consistently achieving R2 values exceeding 0.7 across reviewed studies, thereby demonstrating their efficacy in predicting concrete compressive strength. The integration of ANN models is recommended for formulating various civil engineering properties requiring predictive capabilities. Notably, the adoption of AI models reduces both time and resource expenditures by obviating the need for extensive experimental testing, which can otherwise delay construction activities.