Scholarlink

<p>Limestone calcined clay cement (LC<sup>3</sup>) is a promising low-carbon construction material in terms of its comparable mechanical performance to ordinary Portland cement (OPC) but a much less embodied carbon footprint. Previous literature have demonstrated that the large-scale implementation of LC<sup>3</sup> can reduce embodied CO<sub>2</sub> emissions associated with OPC production by at least 30%. This study proposes a hybrid framework combining machine learning (ML) and multi-objective optimization (MOO) to design cost-effective and eco-friendly LC<sup>3</sup> mixtures. A dataset of 387 LC<sup>3</sup> specimens was constructed to develop ML models for predicting compressive strength. Multivariate Imputation by Chained Equations-Extreme Gradient Boosting (MICE-XGBoost) model achieved the highest accuracy of R<sup>2</sup> = 0.928 (± 0.009). SHAP analysis identified key factors influencing strength, including water-to-cement/binder ratio, and kaolinite content. The local range of each feature showing more significant contributions was also identified. Non-dominated Sorting Genetic Algorithm-II was employed for MOO, generating Pareto fronts to minimize cost and embodied carbon while meeting strength requirements. A minimum balanced reduction in cost by 13.06% and embodied carbon by 14.83% was obtained. Inflection points on Pareto fronts were identified to guide decision-making for low-medium grade mixtures. A table of optimal mix designs is provided, offering practical solutions for selecting sustainable LC<sup>3</sup> formulations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A hybrid prediction and multi-objective optimization framework for limestone calcined clay cement concrete mixture design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

查看全文
  • Xi Chen,
  • Weiyi Chen,
  • Zongao Li,
  • Pu Zhang

摘要

Limestone calcined clay cement (LC3) is a promising low-carbon construction material in terms of its comparable mechanical performance to ordinary Portland cement (OPC) but a much less embodied carbon footprint. Previous literature have demonstrated that the large-scale implementation of LC3 can reduce embodied CO2 emissions associated with OPC production by at least 30%. This study proposes a hybrid framework combining machine learning (ML) and multi-objective optimization (MOO) to design cost-effective and eco-friendly LC3 mixtures. A dataset of 387 LC3 specimens was constructed to develop ML models for predicting compressive strength. Multivariate Imputation by Chained Equations-Extreme Gradient Boosting (MICE-XGBoost) model achieved the highest accuracy of R2 = 0.928 (± 0.009). SHAP analysis identified key factors influencing strength, including water-to-cement/binder ratio, and kaolinite content. The local range of each feature showing more significant contributions was also identified. Non-dominated Sorting Genetic Algorithm-II was employed for MOO, generating Pareto fronts to minimize cost and embodied carbon while meeting strength requirements. A minimum balanced reduction in cost by 13.06% and embodied carbon by 14.83% was obtained. Inflection points on Pareto fronts were identified to guide decision-making for low-medium grade mixtures. A table of optimal mix designs is provided, offering practical solutions for selecting sustainable LC3 formulations.