Prediction of compressive strength and feature importance analysis of solid waste alkali-activated cementitious materials based on machine learning
Prediction of compressive strength and feature importance analysis of solid waste alkali-activated cementitious materials based on machine learning
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DOI：
10.1016/j.conbuildmat.2023.133545
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发表时间：
2023-12
影响因子：
7.4
通讯作者：
Yongjie Ding;Wei Wei-Wei;Jiaojiao Wang;Yanghui Wang;Yuxin Shi;Zijun Mei
中科院分区：
文献类型：
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作者：
Yongjie Ding;Wei Wei-Wei;Jiaojiao Wang;Yanghui Wang;Yuxin Shi;Zijun Mei
本研究探讨了利用机器学习技术分析和处理不同类型的固体废物碱激发胶凝材料。考虑到固体废弃物碱激发胶凝材料的可变性，选择三种关键化学组分（三氧化二铝、氧化钙和二氧化硅）作为代表性特征。共收集了274个相关数据集，并以80%比20%的比例分成训练集和测试集进行分析。采用多层前向神经网络、遗传算法神经网络、支持向量机、随机森林、径向基函数神经网络和长短期记忆网络等6种机器学习模型构建预测模型。通过网格搜索确定最佳超参数。使用训练集和测试集评估了模型的性能，结果表明所有模型在两个集上都表现出很强的预测和泛化能力。其中，支持向量机模型的性能最高，得到的R2值为0.9054，MAE为4.1460，NRMSE为0.0997。通过SHAP（Shapley Additive Decomposition）分析，考察了各因素之间的相互作用和特征的显著性。氧化钙、水胶比、二氧化硅、水玻璃模量和三氧化二铝被认为是影响抗压强度的主要因素。研究结果为优化固体废弃物碱激发胶凝材料的性能提供了指导。
This study examines the analysis and treatment of diverse types of solid waste alkaline-activated cementitious materials utilizing machine learning techniques. To account for the variability of solid waste alkaline-activated cementitious materials, three crucial chemical components (aluminum trioxide, calcium oxide, and silicon dioxide) were chosen as representative features. A total of 274 pertinent datasets were collected and split into training and testing sets with an 80% to 20% ratio for analysis. Six machine learning models, including multilayer feedforward neural network, genetic algorithm neural network, support vector machine, random forest, radial basis function neural network, and long short-term memory network, were employed to construct predictive models. The optimal hyperparameters were identified via grid search. The performance of the models was assessed using the training and testing sets, revealing that all models exhibited strong predictive and generalization capabilities on both sets. Among the models, the support vector machine model achieved the highest performance, yielding anR2value of 0.9054,MAEof 4.1460, andNRMSEof 0.0997. Through the utilization of SHAP (Shapley Additive Explanations) analysis, the interplay between factors and the significance of features were examined. Calcium oxide, water-to-binder ratio, silicon dioxide, modulus of water glass, and aluminum trioxide were recognized as the primary factors that influence the compressive strength. The research findings offer guidance for optimizing the performance of alkaline-activated cementitious materials made from solid waste.