Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete

• Machine learning models showed strong predictive accuracy for UHPC compressive strength. • XGB outperformed RF, GB, and GPR with the highest R-value and the lowest RMSE. • Curing age, silica fume, and fiber content positively impact UHPC strength. Ultra-High-Performance Concrete (UHPC) is distinguished by its exceptional mechanical strength and durability, making it a preferred material for high-performance structural applications. However, the high cement content required for achieving these properties significantly contributes to carbon emissions, posing environmental concerns. To enhance the sustainability of UHPC, it is imperative to develop strategies for reducing cement consumption while maintaining…