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使用集成机器学习方法和SHAP方法对玄武岩纤维增强混凝土分裂拉力强度进行可解释的预测建模
Celal Cakiroglu1, Yaren Aydın2, Gebrail Bekdaş2
1Department of Civil Engineering, Turkish-German University, 34820 Istanbul, Turkey.
Materials (Basel, Switzerland)
|July 14, 2023
概括
这项研究使用玄武岩纤维提高了混凝土的性能. 先进的机器学习模型准确地预测了具体的具体情况.
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.
- 计算力学 计算力学 计算力学
背景情况:
- 玄武岩纤维增强了混凝土的性能,如强度和耐久性.
- 高拉伸强度的玄武岩纤维显著提高了混凝土的分裂拉伸强度.
- 预测混凝土的裂纹抗拉强度对于结构应用至关重要.
研究的目的:
- 使用整体机器学习预测基岩纤维增强混凝土的裂纹抗拉强度.
- 为了评估极端梯度提升 (XGBoost),轻梯度提升机 (LightGBM),随机森林和分类提升 (CatBoost) 的性能.
- 用SHAP和ICE图表分析输入特征对预测准确性的影响.
主要方法:
- 收集并策划了基岩纤维增强混凝土裂解试验的实验数据.
- 应用集体学习算法:XGBoost,LightGBM,随机森林和CatBoost. 这三种算法都在应用中.
- 使用性能指标 (RMSE,MAE,R2) 和SHAP/ICE进行分析.
主要成果:
- XGBoost 取得了大于 0.9.9 的确定系数 (R2).
- 所有测试的组合模型都显示出高预测准确度.
- SHAP和ICE图表提供了对特征重要性和模型行为的见解.
结论:
- 整体机器学习,特别是XGBoost,有效地预测了基岩纤维增强混凝土的分裂抗拉强度.
- 该研究验证了先进的计算方法用于材料性能预测的使用.
- 了解特征影响可以提高预测模型的可靠性和可解释性.
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