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<p>This study presents a clear machine learning framework aimed at forecasting the mechanical properties of environmentally sustainable geopolymer concrete (GPC) made from Ground Granulated Blast Furnace Slag (GGBS) and Sugarcane Bagasse Ash (SCBA). Four ensemble machine learning models: Random Forest (RF), AdaBoost, Gradient Boosting (GB) and XGBoost (XGB) were employed to estimate the Compressive Strength (CS), Split Tensile Strength (STS) and Flexural Strength (FS). Particle Swarm Optimization (PSO) and Bat Optimization Algorithm (BAT) algorithms were employed to optimize the hyperparameter of the model. The best test predictive accuracy with <i>R</i><sup>2</sup> values for CS, STS and FS are 0.983 (GB-BAT), 0.991 (RF-BAT) and 0.985 (XGB-PSO) respectively with lower error metrics. To improve the model’s interpretability, we used SHapley Additive exPlanations and sensitivity analysis. The findings indicated that the anticipated results were significantly influenced by the GGBS content, curing duration and molarity. The study emphasizes a synergistic effect between GGBS replacement and curing age in enhancing strength development. Integrating explainable Artificial Intelligence (AI) with predictive modeling enhances clarity and provides a reliable way to get results without having lot of laboratory work. This framework is a useful tool for designing mixes based on data and encourages eco-friendly methods of building with cement-free concrete.</p>

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Explainable machine learning for predicting the mechanical strength of eco-friendly geopolymer concrete

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • Veerabhadrappa Algur,
  • Poornima Hulipalled,
  • K. Shiva Kumar,
  • J. M. Srishaila,
  • V. Sharvani,
  • H. M. Zameer Ahamed

摘要

This study presents a clear machine learning framework aimed at forecasting the mechanical properties of environmentally sustainable geopolymer concrete (GPC) made from Ground Granulated Blast Furnace Slag (GGBS) and Sugarcane Bagasse Ash (SCBA). Four ensemble machine learning models: Random Forest (RF), AdaBoost, Gradient Boosting (GB) and XGBoost (XGB) were employed to estimate the Compressive Strength (CS), Split Tensile Strength (STS) and Flexural Strength (FS). Particle Swarm Optimization (PSO) and Bat Optimization Algorithm (BAT) algorithms were employed to optimize the hyperparameter of the model. The best test predictive accuracy with R2 values for CS, STS and FS are 0.983 (GB-BAT), 0.991 (RF-BAT) and 0.985 (XGB-PSO) respectively with lower error metrics. To improve the model’s interpretability, we used SHapley Additive exPlanations and sensitivity analysis. The findings indicated that the anticipated results were significantly influenced by the GGBS content, curing duration and molarity. The study emphasizes a synergistic effect between GGBS replacement and curing age in enhancing strength development. Integrating explainable Artificial Intelligence (AI) with predictive modeling enhances clarity and provides a reliable way to get results without having lot of laboratory work. This framework is a useful tool for designing mixes based on data and encourages eco-friendly methods of building with cement-free concrete.