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Machine learning based predicting the dimensionless fracture parameters from different bend-type specimens

delete2025-12-06
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PRE
AI
X
Xinmin Hong
H
Haytham F. Isleem *
DOI：10.1007/s43452-025-01393-2delete
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摘要

摘要

En 中文
在线性弹性断裂力学（LEFM）的范围内，存在多个无量纲参数，称为几何因子。计算几何因子是一个耗时且计算资源密集的过程。本研究评估了各种机器学习模型对无量纲断裂参数（YI、YII、T*）的预测能力。为此，考虑了三种断裂试样，包括单边缺口弯曲（SENB）、半圆形弯曲（SCB）和边缘缺口圆盘弯曲（ENDB）。评估在完整的I/II型脆性断裂条件下进行。作为第一步，对数据集进行了统计描述，包括分布评估、参数相关性以及数据集对输入变量敏感性的检验。使用多样化的数据集，本研究测试了线性模型、Lasso、Ridge、偏最小二乘法、决策树、人工神经网络、随机森林、梯度提升机（GBM）、提升树、支持向量回归和高斯过程回归模型。假设不同的训练/测试比例，以评估模型对训练比例的依赖性。
Keyword:
Mode I/II rock fracture toughness
Machine learning algorithms
Sensitivity analysis
Probabilistic and ensemble models
Bend specimens

期刊

Archives of Civil and Mechanical Engineering 封面图
Archives of Civil and Mechanical Engineering
IF:
4.4
论文数:
2.5K
被引数:
6.2K

机构

S
School of Intelligent Construction
学者数:
10
论文数: 8
被引数: 0
S
School of Civil Engineering
学者数:
2.1K
论文数: 826
被引数: 2
引用论文

引用论文

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