Mechanical assessment with data-driven hybrid machine learning-based optimization of compressive strength of sustainable biochar-concrete composite

Abstract

The rapid rise in global population and industrial activity has intensified environmental challenges, particularly carbon dioxide (CO₂) emissions from the cement and concrete industry. Biochar, a carbon-rich byproduct of biomass pyrolysis, has emerged as a promising solution for sustainable construction by enhancing carbon sequestration and improving mechanical performance when partially substituting cement. This study integrates experimental evidence with advanced machine learning (ML) techniques to evaluate the compressive strength, cost-efficiency, and carbon footprint of biochar-incorporated concrete. A comprehensive dataset of nine input parameters, including cement, aggregates, silica fume, fly ash, biochar, water, superplasticizer, and curing age was modeled using multiple ML approaches. Among the models tested, the hybrid XGB–Histogram Gradient Boosting (XGB-HistGB) model consistently achieved the best overall performance, with a testing R 2 of 0.958, the lowest mean absolute error (3.03), and minimal prediction bias. This model outperformed standalone algorithms and other hybrids, providing reliable accuracy across compressive strength, cost, and embodied CO₂ predictions. SHAP and partial dependence analyses confirmed fine aggregate, curing age, and superplasticizer as the most influential parameters, while biochar dosage required careful optimization to balance strength retention with sustainability benefits. A user-friendly graphical interface was also developed, enabling real-time prediction of compressive strength, material cost, and CO₂ emissions based on user-defined mix proportions. Overall, the findings demonstrate that biochar can be effectively integrated into sustainable concrete formulations, and the XGB-HistGB model offers a powerful AI-driven predictive framework to optimize both structural performance and environmental outcomes.