对机器学习方法进行比较研究,用于预测形曲折样本中的模式I和II脆性骨折
Linfeng Li1, Haytham F Isleem2, Mohammad Khishe3,4
1Department of Civil Engineering, Changzhi Vocational and Technical College, Changzhi, 046000, Shanxi Province, China.
Scientific reports
|October 17, 2025
概括
先进的机器学习模型,如随机森林回归和双向长期短期记忆网络,可以准确地预测单边的曲标本中的断裂参数,优于传统的回归方法.
科学领域:
- 断裂力学 断裂力学 断裂力学
- 计算材料科学科学 计算材料科学
- 在工程领域的人工智能.
背景情况:
- 精确估计断裂参数对于评估材料的结构完整性至关重要.
- 单边切割曲 (SENB) 标本被广泛用于研究混合模式负载下的脆性骨折.
- 传统的统计模型往往难以捕捉碎裂力学中复杂的非线性关系.
研究的目的:
- 评估统计,人工神经网络 (ANN) 和深度学习 (DL) 方法对骨折参数建模的有效性.
- 为了对各种建模方法的准确性,计算效率和概括性进行基准测试.
- 在SENB标本中开发无维断裂参数 (YI,YII,T*) 的预测模型.
主要方法:
- 模型的开发和比较,包括多重线性回归 (MLR),多项式回归 (PR),随机森林回归 (RFR) 和双向长短期记忆 (BiLSTM) 网络.
- 使用了涵盖SENB标本多种几何配置和加载条件的综合数据集.
- 基于培训和验证集的R2值的绩效评估.
主要成果:
- 在训练R2为0.99 (YI,YII) 和验证R2为0.93 (YI),0.96 (YII),0.99 (T*) 的情况下,RFR实现了高精度.
- BiLSTM表现出强大的性能,其验证R2为0.99 (YI),0.96 (YII) 和0.99 (T*).
- 简单回归方法 (MLR,PR) 的有效性有限,R2值较低 (例如,YI的MLR为0.44).
结论:
- 机器学习,特别是RFR和BiLSTM,在SENB标本中预测断裂参数的传统回归方法的性能明显优于传统回归方法.
- 这些先进的模型有效地捕捉了脆性骨折的复杂,非线性行为.
- 开发的模型为工程应用中的断裂分析提供了更准确,更有效的方法.
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