Scholarlink

<p>This investigation develops a comprehensive framework that integrates machine learning modeling with experimental validation for predicting the compressive strength of self-compacting concrete (SCC). The predictive models were constructed using a consolidated data set from existing literature and validated through independent laboratory experiments on ternary blends incorporating fly ash and silica fume. Experimental results demonstrated that optimized combinations of 30% fly ash and 5%–7.5% silica fume produced superior fresh-state properties, including enhanced flowability and reduced segregation, while achieving substantial improvements in compressive, tensile, and flexural strengths across all curing ages. Advanced ensemble learning techniques were employed by coupling Extreme Gradient Boosting (XGBoost) with three metaheuristic optimization algorithms: Tuna Swarm Optimization (TSO), Coyote Optimization Algorithm, and Giant Trevally Optimization. The XGB-TSO model demonstrated superior predictive performance, achieving coefficient of determination <i>R</i><sup>2</sup> = 0.9690, root mean square error of 2.15 MPa, weighted mean absolute percentage error of 2.63%, and Nash-Sutcliffe efficiency of 0.9674. Model interpretability analysis using SHapley Additive explanations (SHAP) identified concrete age and cement content as the most influential parameters, providing transparent insights into strength development mechanisms. Taylor diagram analysis confirmed statistical robustness through high correlation coefficients and minimal centered root mean square error. A graphical user interface was developed to enable real-time strength prediction from standard mix parameters, facilitating practical implementation. Experimental validation using laboratory-produced SCC specimens demonstrated excellent agreement with model predictions, achieving validation <i>R</i><sup>2</sup> = 0.9698 and mean absolute error of 1.83 MPa. The strong correlation between predicted and measured values validates the framework’s reliability for engineering applications. This research advances concrete technology by providing a validated, interpretable, and deployable tool that combines data-driven modeling with experimental verification, enabling intelligent mix design and informed decision-making in sustainable concrete construction.</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

Data-driven and experimental synergy: Metaheuristic-optimized extreme gradient boosting modeling of self-compacting concrete compressive strength

  • 期刊论文

  • 发表日期：

    2026年3月1日

查看全文
  • Divi Sai Vardhan,
  • Akhilendra Sharma,
  • Rahul Biswas,
  • Akshay Bura

摘要

This investigation develops a comprehensive framework that integrates machine learning modeling with experimental validation for predicting the compressive strength of self-compacting concrete (SCC). The predictive models were constructed using a consolidated data set from existing literature and validated through independent laboratory experiments on ternary blends incorporating fly ash and silica fume. Experimental results demonstrated that optimized combinations of 30% fly ash and 5%–7.5% silica fume produced superior fresh-state properties, including enhanced flowability and reduced segregation, while achieving substantial improvements in compressive, tensile, and flexural strengths across all curing ages. Advanced ensemble learning techniques were employed by coupling Extreme Gradient Boosting (XGBoost) with three metaheuristic optimization algorithms: Tuna Swarm Optimization (TSO), Coyote Optimization Algorithm, and Giant Trevally Optimization. The XGB-TSO model demonstrated superior predictive performance, achieving coefficient of determination R2 = 0.9690, root mean square error of 2.15 MPa, weighted mean absolute percentage error of 2.63%, and Nash-Sutcliffe efficiency of 0.9674. Model interpretability analysis using SHapley Additive explanations (SHAP) identified concrete age and cement content as the most influential parameters, providing transparent insights into strength development mechanisms. Taylor diagram analysis confirmed statistical robustness through high correlation coefficients and minimal centered root mean square error. A graphical user interface was developed to enable real-time strength prediction from standard mix parameters, facilitating practical implementation. Experimental validation using laboratory-produced SCC specimens demonstrated excellent agreement with model predictions, achieving validation R2 = 0.9698 and mean absolute error of 1.83 MPa. The strong correlation between predicted and measured values validates the framework’s reliability for engineering applications. This research advances concrete technology by providing a validated, interpretable, and deployable tool that combines data-driven modeling with experimental verification, enabling intelligent mix design and informed decision-making in sustainable concrete construction.