An ensemble approach to predict the compressive strength of Ultra-High-Performance Concrete

Abstract

Ultra-High-Performance Concrete (UHPC) is a state-of-the-art concrete technology with exceptional qualities, including high compressive strength (CS) and durability. The CS, an essential property of UHPC, is determined through costly, time-consuming studies that require a large amount of material. To overcome these constraints, this work aimed to estimate the CS of UHPC using a range of single- and hybrid-machine learning (ML) methods. For this, five ML models, including Decision Tree (DT), Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), CatBoost, and a stacking Ensemble model combining these base models, were developed. Cement, silica fume, slag, fly ash, quartz powder, limestone powder, nano-silica, water, fine and coarse aggregate, fiber, superplasticizer, temperature, relative humidity, and age are all included in the input space. The findings of this study demonstrate that, in terms of prediction accuracy, the Ensemble model outperformed single models, with RMSE, MAE, and R² values of 6.236, 4.733, and 0.977, respectively. According to the findings of 1D, 2D partial dependence plot (PDP), and SHAP analyses, age, cement, silica fume, water, sand, fiber, and superplasticizer were the primary variables influencing UHPC's CS.