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 large amounts 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. The input space includes cement, silica fume, slag, fly ash, quartz powder, limestone powder, nano-silica, water, fine and coarse aggregate, fiber, superplasticizer, temperature, relative humidity, and age. The findings demonstrate that the Ensemble model achieved the best overall predictive performance across 20 Monte Carlo simulations, with a mean RMSE of 6.751 ± 0.316 MPa, MAE of 4.943 ± 0.219 MPa, and R² of 0.972 ± 0.002. 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.