Predicting the compressive strength of rubberized concrete containing silica fume using stacking ensemble learning model

Abstract

As the construction industry advances toward sustainability, rubberized concrete emerges as a promising material due to its potential for recycling waste rubber. While silica fume (SF) is often used to address the reduced compressive strength resulting from rubber integration, the complex interactions between these materials present significant modeling challenges. This study employs a novel machine learning approach to effectively capture these interactions and accurately predict the compressive strength of SF-enhanced rubberized concrete. Utilizing a dataset comprising 237 experimental data points curated from 25 research studies, an advanced stacking ensemble model was developed. This model features a trainable meta-structure that integrates diverse, hyper-tuned base learners, including Decision Trees (DT), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) regressors. Hyperparameter tuning, performed using 10-fold cross-validation, was applied to enhance overall model performance. The findings show that XGBoost outperformed other base models, achieving an overall Coefficient of Determination (R2) of 0.9194 and a Mean Squared Error (MSE) of 10.5625. The stacking approach, with KNN as the meta-learner, further refined individual model performances, resulting in an improved R2 of 0.9397 and an MSE of 7.1671 on the testing data. Compared to traditional voting ensemble techniques, the stacking models offered a more nuanced enhancement of predictive outcomes, while the averaging ensembles were noted for their simplicity and competitive accuracy. Additionally, feature importance analysis using SHapley Additive exPlanations (SHAP) revealed that superplasticizer, rubber content, and SF were the most influential inputs in the developed model.