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灵敏工程材料的动态强度预测通过堆叠多模型合集学习和可解释性驱动的特征分析
Xin Cai1,2, Yunmin Wang1,2, Yihan Zhao3
1Sinosteel Maanshan General Institute of Mining Research Co., Ltd., Maanshan 243000, China.
Materials (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一种新的机器学习框架,用于预测脆性材料的动态压力强度,提高精度和理解压力下的材料行为.
科学领域:
- 地质技术工程 地质技术工程
- 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:
- 准确预测易碎材料的动态压力强度对于地下工程和安全至关重要.
- 现有的模型对于复杂的物质行为可能缺乏准确性和解释性.
研究的目的:
- 开发一个增强的机器学习框架,用于预测动态压力强度.
- 通过使用集体学习和SHAP来提高模型的准确性和可解释性.
- 确定影响材料动态强度的关键因素.
主要方法:
- 开发了一个新的堆叠合奏学习框架.
- 六个回归模型 (KNN,RF,GBDT,LightGBM,XGBoost,MLPNN) 被训练和评估.
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
主要成果:
- 拟议的堆叠模型在预测准确性,稳定性和概括性方面明显优于个别基准模型.
- 应变率被确定为影响动态强度预测的主导因素.
- 发现静态强度,P波速度和散装密度是显著的贡献者.
结论:
- 开发的框架提供了一个强大的和可解释的工具,用于预测易碎材料的动力强度.
- 这些发现为在动态负载下材料的机械行为提供了宝贵的见解.
- 这种方法支持在地下工程中加强安全评估和设计.
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