Using artificial intelligence-based tools, this research aims to establish a direct correlation between the alkali-activated concrete (AAC) mix design factors and their performances. More specifically, the machine learning system was fed new property data obtained from AAC mixes used in laboratory experiments. The rheological parameters (yield stress [static/dynamic] and plastic viscosity) of AAC were predicted using the multilayer perceptron neural network (MLPNN) and bagging ensemble (BE) models. In addition, the R 2 values, k-fold analyses, statistical checks, and the dissimilarity between the experimental and predicted compressive strength were employed to assess the performance of the created models. Also, the SHapley additive exPlanation (SHAP) approach was used for examining the relevance of influencing parameters. The BE approach was found to be significantly accurate in all prediction models, with R 2 greater than 0.90, and MLPNN models were found to be moderately precise, with R 2 slightly below 0.90. However, the error assessment through statistical checks and k-fold analysis also validated the higher precision of BE models over the MLPNN models. Building models that can calculate rheological properties of AAC for different values of input parameters could save a lot of time and money compared to doing the tests in a laboratory. In order to ascertain the required amounts of raw materials of AAC, investigators, as well as businesses, may find the SHAP study helpful.

中文翻译：

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使用当代人工智能方法研究碱激活混凝土的流变特性

本研究旨在使用基于人工智能的工具，建立碱激活混凝土（AAC）配合比设计因素与其性能之间的直接关联。更具体地说，机器学习系统输入了从实验室实验中使用的 AAC 混合物中获得的新属性数据。使用多层感知器神经网络 (MLPNN) 和装袋集成 (BE) 模型预测 AAC 的流变参数（屈服应力 [静态/动态] 和塑性粘度）。除此之外右 2采用数值、k 倍分析、统计检查以及实验和预测抗压强度之间的差异来评估所创建模型的性能。此外，还使用 ​​SHapley 附加解释 (SHAP) 方法来检查影响参数的相关性。我们发现 BE 方法在所有预测模型中都非常准确，右 2大于 0.90，并且发现 MLPNN 模型具有中等精度，其中右 2略低于0.90。然而，通过统计检查和 k 倍分析进行的误差评估也验证了 BE 模型比 MLPNN 模型具有更高的精度。与在实验室进行测试相比，建立可以计算不同输入参数值的 AAC 流变特性的模型可以节省大量时间和金钱。为了确定 AAC 原材料的需求量，研究人员以及企业可能会发现 SHAP 研究很有帮助。