Developing machine learning frameworks to predict mechanical properties of ultra-high performance concrete mixed with various industrial byproducts

Abstract

This research investigates the predictive modeling of ultra-high-performance concrete (UHPC) incorporating industrial byproducts, focusing on compressive strength (Fc), flexural strength (Ff), workability (Slump), and porosity. Various machine learning models, including Kstar, M5Rules, ElasticNet, XNV, and Decision Table (DT), were evaluated to determine the most accurate predictions for each property. The findings indicate that Kstar outperformed other models across all categories, demonstrating the highest accuracy and lowest errors in predicting Fc, Ff, Slump, and Porosity. Sensitivity analyses using SHAP and Hoffman & Gardener's methods identified the most influential parameters affecting each UHPC property, providing insights into the key factors driving concrete performance. The research highlights the sustainable impact of incorporating industrial byproducts, reducing environmental footprints while maintaining superior mechanical and durability properties. The developed models offer practical guidance for optimizing UHPC formulations, promoting sustainability in the construction industry by enabling efficient material utilization and reducing reliance on conventional cement-based components.