机器学习算法的应用用于使用贝叶斯优化来测量钢筋和混凝土之间的结合强度
Huajun Yan1, Nan Xie1, Dandan Shen2
1School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China.
Materials (Basel, Switzerland)
|September 28, 2024
概括
机器学习模型可以准确地预测钢混凝土的结合强度. 与XGBoost相结合的贝叶斯优化提供了最精确的估计,改善了结构工程应用.
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
- 计算科学 计算科学
背景情况:
- 钢筋和混凝土之间的结合强度对于结构完整性至关重要.
- 现有的经验模型可能无法完全捕捉影响债券行为的复杂因素.
- 准确预测粘合强度对于安全高效的结构设计至关重要.
研究的目的:
- 用机器学习 (ML) 算法估计钢筋与混凝土之间的粘合强度.
- 为了比较不同ML算法的性能,包括随机森林 (RF),支持向量回归 (SVR) 和极端梯度增强 (XGBoost),用贝叶斯优化 (BO) 增强.
- 开发一个简化的ML模型,用于结构工程中的实际应用.
主要方法:
- 利用了401个光束测试的数据集,其中包含了六个冲击因子.
- 实现并比较了与贝叶斯优化 (BO) 集成的RF,SVR和XGBoost算法.
- 采用沙普利增量解释 (SHAP) 来解释ML模型的预测.
主要成果:
- 与经验模型相比,贝叶斯优化-XGBoost (BO-XGBoost) 模型显示出更高的准确性.
- 在测试组中,BO-XGBoost 实现了 R2,MAE 和 RMSE 值分别为 0.87,0.897 MPa 和 1.516 MPa.
- 为了方便使用,开发了一个采用三个输入变量 (钢筋直径,屈服强度,混凝土压力强度) 的简化模型.
结论:
- 机器学习,特别是BO-XGBoost,提供了一个非常准确的方法来预测钢混凝土的结合强度.
- 开发的简化模型增强了ML在结构分析中的实际应用.
- 机器学习算法可以有效地预测复杂的界面机械行为,从而提高模型的准确性和可靠性.
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