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A Study on Constraint-Related Fracture Toughness Prediction Based on Random Forest Algorithm and Data Enhancement Strategies
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Kangzhong Shan1, Xiaoxiao Wang2, Fang Liu3, Yuanyuan Cui3, Jie Yang1, **
Chinese Journal of Solid Mechanics | 2025, 46(1) : 105 - 116
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Chinese Journal of Solid Mechanics | 2025, 46(1): 105-116
Research Papers
A Study on Constraint-Related Fracture Toughness Prediction Based on Random Forest Algorithm and Data Enhancement Strategies
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Kangzhong Shan1, Xiaoxiao Wang2, Fang Liu3, Yuanyuan Cui3, Jie Yang1, **
Affiliations
  • 1Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, 200093
  • 2Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai, 200237
  • 3School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093
Published: 2025-02-28 doi: 10.19636/j.cnki.cjsm42-1250/o3.2024.044
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The study of data-driven predictions for constraint-related fracture toughness is an interdisciplinary scientific problem relevant to mechanics, mechanical engineering, as well as computer science and technology, and is of great significance for accurate structural integrity assessment. This research focused on nuclear power steel A508. The predictive capabilities of four algorithms, namely the K-nearest neighbors (KNN) regression, kernel regression (KR), linear regression (LR), and random forest (RF) regression, for constraint-related fracture toughness predictions were investigated. The RF algorithm outperformed the others, while the KR algorithm had the least effective predictions. The prediction accuracy ranked as follows: RF>LR>KNN>KR. Furthermore, based on the RF algorithm, data under plane strain conditions were added for data enhancement, enabling the prediction and verification of constraint-related fracture toughness for single-edge notch bending (SENB) specimens. The validated model was successfully transplanted to single-edge notch tension (SENT), compact tension (CT), and central crack tension (CCT) specimens. Results indicated that the RF algorithm with data augmentation improved prediction accuracy and capability, particularly at boundary points. The RF-based model, enhanced with additional data strategies, demonstrated strong generalization across different specimen types. For SENB and CT specimens, bending loads dominate at the crack tip; thus, altering a/W and B/W enhances restraint. For SENT and CCT specimens, where shear loads predominate at the crack tip, adjusting a and B proves more effective. Finally, a unified, high-accuracy prediction model was developed by incorporating sample category features using the RF algorithm and data enhancement strategies.

constraint  /  fracture toughness  /  machine learning  /  random forest algorithm  /  data enhancement
Kangzhong Shan, Xiaoxiao Wang, Fang Liu, Yuanyuan Cui, Jie Yang. A Study on Constraint-Related Fracture Toughness Prediction Based on Random Forest Algorithm and Data Enhancement Strategies[J]. Chinese Journal of Solid Mechanics, 2025 , 46 (1) : 105 -116 . DOI: 10.19636/j.cnki.cjsm42-1250/o3.2024.044
Year 2025 volume 46 Issue 1
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doi: 10.19636/j.cnki.cjsm42-1250/o3.2024.044
  • Receive Date:2024-09-15
  • Online Date:2026-03-20
  • Published:2025-02-28
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  • Received:2024-09-15
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Affiliations
    1Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, 200093
    2Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai, 200237
    3School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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