Exploring the interrelationships between composition, rheology, and compressive strength of self-compacting concrete: An exploration of explainable boosting algorithms - Sarmed Wahab; Babatunde Abiodun Salami; Ali H. AlAteah; Mohammed M.H. Al-Tholaia; Turki S. Alahmari (Case Studies in Construction Materials, 2024)
This study introduces a novel methodology for enhancing the compressive strength of self-compacting concrete (SCC) via the use of the Explainable Boosting Machine (EBM), a sophisticated and interpretable machine learning algorithm. It presents a data-driven model that aims to accurately predict the strength of SCC by considering the intricate interactions among its various elements. Additionally, the model provides insights into the variables that influence SCC's compressive strength. By using EBM in conjunction with XGBoost and CatBoost algorithms, this study conducts a comparative examination of predictive abilities using datasets related to composition and rheology. The findings reveal that CatBoost has greater predictive performance using rheology dataset, as shown by an R2 value of 0.977. Conversely, XGBoost exhibits a higher predictive capability using the composition dataset, as indicated by an R2 value of 0.947. The EBM can provide comprehensive explanations at both global and local levels. It effectively identifies the key factors that have a significant influence on compressive strength. These factors include the coarse aggregate content, cement content, water content, viscosity, and V-funnel flow time. The study findings provide more evidence to support the notion that including rheological data into the model leads to a notable improvement in its accuracy. This outcome further confirms the existence of a direct correlation between rheological properties and compressive strength. The explanatory insights provided by EBM give practical instructions for customising SCC mixes to attain desired strengths. This facilitates quality control and enables personalised concrete design in the field of construction. This study highlights the potential of interpretable machine learning algorithms in improving the predictive modelling of SCC features. This advancement may lead to the development of more durable, efficient, and customised building materials.