基于机器学习的工程混凝土复合材料的关键机械性能数据驱动的预测
Shuangquan Qing1, Chuanxi Li2,3
1Department of Civil Engineering, Changsha University of Science & Technology, Changsha, 410114, China. doc_qing@stu.csust.edu.cn.
Scientific reports
|July 3, 2024
概括
机器学习可以准确地预测工程混凝土复合材料 (ECC) 的机械性能. 随机森林和XGBoost模型提供精确的估计,指导工程师在材料设计和优化以提高性能.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 工程水泥复合材料 (ECCs) 需要精确的机械性能预测,以获得最佳的设计.
- 预测ECC属性的现有方法可能是耗时和资源密集的.
- 使用机器学习的数据驱动方法为高效的财产估计提供了新的解决方案.
研究的目的:
- 开发和优化机器学习模型,用于预测ECC的关键机械性质.
- 确定影响ECC机械性能最有影响的特征.
- 建立基于预测建模的ECC机械性能控制策略.
主要方法:
- 编制和分析1532个ECC数据集.
- 实施和比较四个机器学习算法:线性回归 (LR),K-最近邻居 (KNN),随机森林 (RF) 和极端梯度增强 (XGB).
- 在特征重要性分析中应用夏普利添加式解释 (SHAP).
主要成果:
- 射频模型实现了0.92的压力强度和0.91的屈曲强度的R2.
- XGB模型实现了拉伸强度为0.87的R2和拉伸延伸能力为0.80.
- 水-水泥比率 (W) 和降水器 (WR) 显著影响压力和拉伸强度;WR是压力强度的关键,聚乙烯 (PE) 纤维是其他特性.
结论:
- 机器学习模型,特别是RF和XGB,提供了ECC机械性能的准确预测.
- 功能重要性分析显示,WR和PE纤维是影响ECC性能的关键因素.
- 开发的控制策略为工程师在设计具有所需机械特性的ECC时提供了实际指导.
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