Scholarlink

<p>The present study intends to study the mechanical properties of fiber-reinforced geopolymer concrete (FRGC) experimentally and validate the results using state-of-the-art machine learning. At 28 days, compressive strength (CS) of 72.6&#xa0;MPa, and split tensile strength (STS) of 9.4&#xa0;MPa were recorded. The CS deteriorated with a further increase in the NF content due to the cluster formation, while the STS increased with increasing fiber dosage. The hybrid ML models, including Random Forest (RF) and XGBoost (XGB), were fine-tuned using Grid Search (GS) and Giant Armadillo (GA) algorithm based on 5-fold cross-validation. The GA-XGB model had maximum accuracy to predict CS (R² = 0.988, RMSE = 0.032&#xa0;MPa) and STS (R² = 0.985, RMSE = 0.029&#xa0;MPa) in testing sets. SHAP analysis supported molarity and SS/SH as the influencing factor for CS, and meanwhile, NF for STS. Shapley additive explanations (SHAP), Partial dependence (PDP) and Individual Conditional Expectation (ICE) plots confirmed these trends by showing the nonlinear effects of each independent variable on strength predictions. A graphical user interface (GUI) was also designed to aid practical use, which the user can use to insert long short mix parameters and receive instant predictions for CS and STS. The close match between experimental measurements and ML predictions, along with the importance of explainability and GUI integration, proves that the developed FRGPC design framework is robust, transparent and usable in real applications.</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

Experimental insights and hybridized ensemble machine learning validation of fiber reinforced geopolymer concrete strength

  • 期刊论文

  • 发表日期：

    2025年10月15日

查看全文
  • Suebha Khatoon,
  • Kaliluthin A K,
  • Sanjog Chhetri Sapkota

摘要

The present study intends to study the mechanical properties of fiber-reinforced geopolymer concrete (FRGC) experimentally and validate the results using state-of-the-art machine learning. At 28 days, compressive strength (CS) of 72.6 MPa, and split tensile strength (STS) of 9.4 MPa were recorded. The CS deteriorated with a further increase in the NF content due to the cluster formation, while the STS increased with increasing fiber dosage. The hybrid ML models, including Random Forest (RF) and XGBoost (XGB), were fine-tuned using Grid Search (GS) and Giant Armadillo (GA) algorithm based on 5-fold cross-validation. The GA-XGB model had maximum accuracy to predict CS (R² = 0.988, RMSE = 0.032 MPa) and STS (R² = 0.985, RMSE = 0.029 MPa) in testing sets. SHAP analysis supported molarity and SS/SH as the influencing factor for CS, and meanwhile, NF for STS. Shapley additive explanations (SHAP), Partial dependence (PDP) and Individual Conditional Expectation (ICE) plots confirmed these trends by showing the nonlinear effects of each independent variable on strength predictions. A graphical user interface (GUI) was also designed to aid practical use, which the user can use to insert long short mix parameters and receive instant predictions for CS and STS. The close match between experimental measurements and ML predictions, along with the importance of explainability and GUI integration, proves that the developed FRGPC design framework is robust, transparent and usable in real applications.