AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete.

Ultra-high-performance fiber-reinforced concrete (UHPFRC) is an exceptional type of cementitious composite with superior mechanical and durability performances. Achieving these properties involves maintaining a low water-to-cement ratio, optimizing aggregate size distribution, and integrating fiber reinforcement. Recently, there has been a notable trend in the development and application of UHPFRCs. However, there is still a requirement for artificial intelligence (AI) methods to predict the early-age compressive strength (CS) of UHPFRC and to define the key input factors for optimal mix design with appropriate proportions. Therefore, five AI models were chosen to assess the predictive accuracy of early-age CS in the current study. These models include support vector regression (SVR), random forest (RF), artificial neural network (ANN), gradient boosting (GB), and Gaussian Process Regression (GPR). As part of evaluating model performance and conducting error analysis, this study investigated differences in prediction accuracy among five models across training and testing datasets. Additionally, feature importance analysis was implemented to explore the influence of the input variables on the early-age CS. Results indicate that GPR and SVR models with high predictive accuracy (R