通过使用机器学习方法,间接预测石墨烯纳米板块强化水泥复合材料的压力强度
Muhammad Fawad1,2, Hisham Alabduljabbar3, Furqan Farooq4,5
1Department of Structural Engineering, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 2, 44-100, Gliwice, Poland. Muhammad.Fawad@polsl.pl.
Scientific reports
|June 20, 2024
概括
机器学习模型准确地预测了水泥中石墨烯纳米板块的压力强度. CatBoost 模型显示出极高的效率,并强调了石墨烯纳米板块厚度作为关键因素.
科学领域:
- 材料科学与工程 材料科学与工程
- 土木工程 土木工程是指土木工程.
- 材料中的人工智能
背景情况:
- 石墨烯纳米板块 (GrNs) 提高了水泥复合材料的电导率和强度.
- 传统的模型在纳米级水泥复合材料的复杂性方面扎.
- 对压力强度 (CS) 的准确预测对于材料开发至关重要.
研究的目的:
- 开发强大的机器学习 (ML) 模型来预测基于石墨烯纳米板块的水泥材料的压力强度 (CS).
- 为了比较决策树 (DT),分类提升机 (CatBoost),自适应神经模糊推理系统 (ANFIS) 和光梯度提升机 (LightGBM) 的性能.
- 用SHapley添加式解释 (SHAP) 来解释影响CS的关键因素.
主要方法:
- 从已发表的文献中使用了172个数据点的广泛数据集.
- 采用了四种ML模型:DT,CatBoost,ANFIS和LightGBM用于预测.
- 应用SHAP分析用于模型解释性和特征重要性评估.
主要成果:
- 所有的ML模型都显示出高预测准确度,R值在0.8662到0.9999.9之间.
- CatBoost模型实现了极高的预测效率 (R=0.9999).
- 根据SHAP分析,GrN厚度是影响CS的最重要因素 (SHAP值+9.39),其次是GrN直径,固化年龄和w/c比率.
结论:
- 机器学习方法为预测石墨烯纳米板块-水泥复合材料的性能提供了对实验程序的有效和经济的替代方案.
- 在这些先进材料中,CatBoost模型对于CS预测非常有效.
- 未来的研究应该包含更大的数据集和额外的输入参数,以提高模型的概括性.
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