Leveraging Ensemble Structures to Elucidate the Impact of Factors That Influence the Quality of Ultra-High Performance Concrete

Concrete is an essential material ubiquitously employed in construction. Yet, deciphering the factors that influence its quality is a formidable challenge due to partially understood physical relationships, the high dimensionality of the data, and its limited availability. This study introduces an ensemble framework designed to address these challenges. It uses a combination of individual methods within an ensemble configuration to identify the critical features that determine concrete quality. Within this framework, diverse base methods are harmonized using an average-based technique, leading to a robust final verdict. After selecting the potential influencing factors, 50 experiments are conducted using the Taguchi Orthogonal Array (L-50) to generate the data points. The proposed ensemble learning framework underscores the substantial impact of storage conditions during the curing time on the final quality of concrete.