Popular and eco-friendly alkali-activated materials (AAMs) replace Portland cement concrete. Due to the considerable compositional variability of AAMs and the inability of established materials science methods to understand composition–performance relationships, accurate property forecasts have proved impossible. This study set out to develop AAM compressive strength (CS) evaluation machine learning (ML) models using techniques including extreme gradient boosting (XGB), bagging regressor (BR), and multi-layer perceptron neural network (MLPNN). Ten input variables were used with a large dataset of 676 points. Statistical and K-fold studies were also used to evaluate the developed models’ correctness. XGB predicted the CS of AAM the best, followed by BR and MLPNN. The MLPNN and BR models had R 2 values of 0.80 and 0.90, respectively, whereas the XGB model had 0.94. Results from statistical analyses and k-fold cross-validation of the used ML models further attest to their validity. The built models can potentially compute the CS of AAMs for a variety of input parameter values, reducing the requirement for costly and time-consuming laboratory testing. Researchers and businesses may find this study useful in determining the necessary quantities of AAMs’ raw components.

中文翻译：

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用于可持续建筑的低碳隐含碱激活材料：单个学习者和集体学习者的比较研究

流行且环保的碱激活材料（AAM）取代了波特兰水泥混凝土。由于 AAM 的成分变化很大，而且现有的材料科学方法无法理解成分与性能之间的关系，因此准确的性能预测已被证明是不可能的。本研究旨在使用极限梯度提升 (XGB)、装袋回归器 (BR) 和多层感知器神经网络 (MLPNN) 等技术开发 AAM 抗压强度 (CS) 评估机器学习 (ML) 模型。676 个点的大型数据集使用了 10 个输入变量。统计和 K 折研究也用于评估所开发模型的正确性。XGB 对 AAM 的 CS 的预测效果最好，其次是 BR 和 MLPNN。MLPNN 和 BR 模型右 2值分别为 0.80 和 0.90，而 XGB 模型为 0.94。所使用的 ML 模型的统计分析和 k 倍交叉验证的结果进一步证明了其有效性。构建的模型可以计算 AAM 针对各种输入参数值的 CS，从而减少昂贵且耗时的实验室测试的要求。研究人员和企业可能会发现这项研究有助于确定 AAM 原材料的必要数量。