Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials

Abstract

Compressive strength is a key factor in the design and durability of concrete structures. Accurate prediction of compressive strength helps optimize material use and reduce construction costs. This study proposes a novel stacked model for predicting compressive strength, integrating three base models with linear regression. The base models include Artificial Neural Networks, Random Forest, and Extreme Gradient Boosting, while the stacked model uses Linear Regression as the metamodel. A dataset of 1,030 concrete mix samples covering eight critical input parameters, including cement, blast furnace slag, coarse aggregates, fine aggregates, fly ash, water, superplasticizer, and curing days, was used for training and evaluation. The dataset was split into training (80%), validation (10%), and testing (10%) subsets. The models were trained independently, and their predictions were used to develop the stacked model. Among the base models, the Extreme Gradient Boosting model achieved the highest accuracy, with an R2 of 0.947 during testing. However, the stacked model outperformed it, attaining an R2 of 0.953 in the testing phase. Shapley additive explanations analysis identified curing duration as the most influential factor in compressive strength prediction. A user-friendly graphical interface was developed to facilitate efficient prediction of compressive strength in concrete structures.