可解释的机器学习用于多组件混凝土:预测建模和特征交互见解
Jie Wang1, Junqi Deng1, Siyi Li1
1School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Materials (Basel, Switzerland)
|October 16, 2025
概括
机器学习模型通过分析关键因素来预测混凝土的压力强度. 这种数据驱动的方法增强了工程设计,并促进了可持续的建筑材料.
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.
- 计算机科学 计算机科学
背景情况:
- 多元组件的具体绩效评估依赖于主观的专家判断和漫长的监测.
- 人工智能 (AI) 和机器学习 (ML) 为构建科学提供了先进的数据分析能力.
- 解决来自人为因素的不确定性对于可靠的具体绩效评估至关重要.
研究的目的:
- 通过使用各种机器学习技术,调查影响混凝土压力强度的关键因素.
- 通过SHAP分析提高ML模型在具体科学中的可解释性.
- 为优化具体性能和可持续性提供数据驱动的基础.
主要方法:
- 应用各种机器学习算法:线性回归,多项式回归,决策树,随机森林,ExtraTrees,AdaBoost,CatBoost,XGBoost和TabPFN.
- 使用SHAP (夏普利添加式扩展) 分析来发现特征的重要性和相互作用.
- 数据驱动的调查对多元组件混凝土压力强度的影响因素.
主要成果:
- 通过ML模型分析确定影响混凝土压力强度的关键因素.
- 通过SHAP,更好地了解多元件混凝土系统中的特征重要性和相互作用.
- 展示ML能够提供对具体表现的预测洞察力的能力.
结论:
- 机器学习为评估混凝土压力强度提供了一个强大的,数据驱动的方法.
- SHAP分析提供了有价值的解释性,揭示了具体材料科学的潜在机制.
- 这些发现支持改进工程设计,建设决策和开发可持续的混凝土材料.
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