用矿物粉修饰的青的风学特性预测:袋装,提升和堆叠与堆叠. 单个机器学习模型
Haibing Huang1, Zujie Xu1, Xiaoliang Li1
1Xinyu Highway Survey and Design Institute, Xinyu 338000, China.
Materials (Basel, Switzerland)
|June 27, 2025
概括
集成机器学习模型,特别是堆叠模型,可以准确预测青的质性质. 这种方法优化了青混合物设计和路面性能.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 计算科学 计算科学
背景情况:
- 准确预测青的质性质对于路面性能至关重要.
- 传统的单一机器学习模型在描述复杂的物质行为方面存在局限性.
研究的目的:
- 系统地比较单个机器学习模型和集体方法来预测青的质性质 (复杂的剪切模量G*和相角 δ).
- 开发和验证一个创新的堆叠组合模型,以提高预测准确度.
主要方法:
- 使用乳化剂和矿物粉末制造改性乳化青.
- 使用动态剪切流体计 (DSR) 进行风病学性质测试.
- 使用局部异常因素 (LOF),K折交叉验证和贝叶斯优化进行数据预处理.
- 开发一个堆叠模型,将KNN,决策树,随机森林和XGBoost基础学习者与贝叶斯脊回归元学习者集成在一起.
主要成果:
- 整体模型显著优于单个机器学习模型.
- 堆叠模型实现了最高的预测准确性 (G* 的 R2 = 0.9727 和 δ 的 R2 = 0.9990).
- 沙普利添加物解释 (SHAP) 确定温度和矿物粉类型是关键的预测因素.
结论:
- 开发的堆叠模型为优化青混合物设计提供了一个强大的框架.
- 这种方法通过解决材料科学中机器学习的"黑子"性质,提高了材料选择和路面性能.
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