As the demand for sustainable construction materials grows, one-part geopolymers present a viable alternative due to their potential for enhancing strength while minimizing carbon emissions and costs. However, accurately predicting the compressive strength of these materials poses significant challenges. Traditional predictive methods, including empirical equations and basic regression techniques, often fall short in capturing the complex relationships among compositional variables. This study employs machine learning (ML) techniques to improve the prediction of compressive strength and perform sensitivity analysis for one-part geopolymers. Experimental analyses were conducted to assess compressive strength, microstructure, and pore characteristics, revealing that increased slag replacement rates enhance hardness and porosity, particularly at levels below 60 %. Given the inherent uncertainties in modeling one-part geopolymer strength, six ML models were evaluated using a comprehensive database. The XGB model exhibited excellent performance, achieving an R2 of 0.95 and an RMSE of 5.2 on the test set, with results validated through experimental data. Additionally, feature importance analysis utilizing the SHAP method highlighted slag percentage, activator Na₂O content, and water-cement ratio as critical factors influencing strength. This research provides an effective and interpretable framework for optimizing one-part geopolymer formulations, advancing sustainable practices in construction.

中文翻译：

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使用实验和机器学习方法分析单组分地质聚合物的抗压强度


随着对可持续建筑材料的需求增长，单组分地聚合物提供了一种可行的替代品，因为它们有可能提高强度，同时最大限度地减少碳排放和成本。然而，准确预测这些材料的抗压强度带来了重大挑战。传统的预测方法（包括经验方程和基本回归技术）往往无法捕捉组成变量之间的复杂关系。本研究采用机器学习 （ML） 技术来改进抗压强度的预测，并对单组分地质聚合物进行敏感性分析。进行了实验分析以评估抗压强度、微观结构和孔隙特性，结果表明，炉渣替代率的增加会提高硬度和孔隙率，尤其是在低于 60 % 的水平下。鉴于对单组分地质聚合物强度进行建模的固有不确定性，使用综合数据库评估了六个 ML 模型。XGB 模型表现出优异的性能，在测试集上实现了 0.95 的 R2 和 5.2 的 RMSE，结果通过实验数据验证。此外，利用 SHAP 方法进行的特征重要性分析突出了炉渣百分比、活化剂 Na₂O 含量和水灰比是影响强度的关键因素。这项研究为优化单组分地质聚合物配方提供了一个有效且可解释的框架，从而推进建筑中的可持续实践。