Enhanced Maturity-Strength Model for Predicting Concrete Compressive Strength

Due to the diversity of materials comprising the essential components of concrete and the intricate mix of these components, predicting the strength of concrete poses a challenging task. Researchers have employed a range of tools, including statistical analysis and deep learning techniques, in the development of models to predict concrete strength across various concrete mix design. The compressive strength of concrete is influenced by several factors, and this study centers on variables such as cement type, air content, water-to-cementitious material ratio, quantity of mineral admixture, and the substitution of cement with fly ash or slag. The aim is to construct a predictive model for concrete compressive strength that considers these factors. This predictive analysis employs nonlinear regression analysis and machine learning techniques, with a specific focus on artificial neural networks. The incorporation of the maturity concept addresses the impact of temperature and time on the development of concrete mechanical properties. Utilizing the dataset comprising diverse concrete ages from existing literature, the study performs a comparative analysis among the mentioned models to identify the one that demonstrates the most accurate fit.