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Lightweight Design of Axle Bridge Based on Kriging Model with Optimal Regression.

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Title: Lightweight Design of Axle Bridge Based on Kriging Model with Optimal Regression.
Authors: Li, Xiaoke1 lixiaoke@zzuli.edu.cn, Du, Haiyang1 332404040388@zzuli.edu.cn, Jiang, Qianlong2 JQL0259@163.com, Ma, Jun1 majun@zzuli.edu.cn, Ming, Wuyi1 mingwuyi@gmail.com, Zhu, Heng3 zhuheng@ybsteer.com
Source: IAENG International Journal of Computer Science. May2026, Vol. 53 Issue 5, p1605-1614. 10p.
Subjects: Genetic algorithms, Structural optimization, Regression analysis, Design techniques, Fatigue life, Bridge design & construction, Response surfaces (Statistics)
Abstract: In this paper, a lightweight design method of axle bridge based on Kriging model and genetic algorithm (GA) is proposed. Firstly, 21⁴ samples by full factorial design were selected as the candidate sample library. Then 100 samples were randomly selected as the initial population. Secondly, ANASYS APDL was used to establish the parametric model of axle bridge, which was called to obtain performance responses (including the stress, deformation, and fatigue life) at the initial population. Then the Kriging model was constructed to replace the implicit relationship between the structural parameters and performance responses of axle bridge. To ensure the modeling accuracy, the optimal regression function in Kriging was determined through the correlation coefficient R² and leave one out cross validation (LOOCV). Through selection, crossover and mutation, the optimal individual in each iteration was obtained, and the error between the real value and prediction value by Kriging was calculated. The individual with error larger than the threshold was selected as sequential sample to update the Kriging model. Finally, the optimal axle bridge parameters were obtained. Under the constraints of stress, deformation, and fatigue life, the mass of the axle bridge at the optimal parameters is reduced by 106.5Kg and the mass reduction ratio reaches 22.66%. Therefore, the proposed lightweight method is an effective method to ensure the performance of the axle bridge and reduce the production cost. [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 193482018
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Lightweight Design of Axle Bridge Based on Kriging Model with Optimal Regression.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Xiaoke%22">Li, Xiaoke</searchLink><relatesTo>1</relatesTo><i> lixiaoke@zzuli.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Du%2C+Haiyang%22">Du, Haiyang</searchLink><relatesTo>1</relatesTo><i> 332404040388@zzuli.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Qianlong%22">Jiang, Qianlong</searchLink><relatesTo>2</relatesTo><i> JQL0259@163.com</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Jun%22">Ma, Jun</searchLink><relatesTo>1</relatesTo><i> majun@zzuli.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Ming%2C+Wuyi%22">Ming, Wuyi</searchLink><relatesTo>1</relatesTo><i> mingwuyi@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Heng%22">Zhu, Heng</searchLink><relatesTo>3</relatesTo><i> zhuheng@ybsteer.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. May2026, Vol. 53 Issue 5, p1605-1614. 10p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+optimization%22">Structural optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Design+techniques%22">Design techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Fatigue+life%22">Fatigue life</searchLink><br /><searchLink fieldCode="DE" term="%22Bridge+design+%26+construction%22">Bridge design & construction</searchLink><br /><searchLink fieldCode="DE" term="%22Response+surfaces+%28Statistics%29%22">Response surfaces (Statistics)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, a lightweight design method of axle bridge based on Kriging model and genetic algorithm (GA) is proposed. Firstly, 21⁴ samples by full factorial design were selected as the candidate sample library. Then 100 samples were randomly selected as the initial population. Secondly, ANASYS APDL was used to establish the parametric model of axle bridge, which was called to obtain performance responses (including the stress, deformation, and fatigue life) at the initial population. Then the Kriging model was constructed to replace the implicit relationship between the structural parameters and performance responses of axle bridge. To ensure the modeling accuracy, the optimal regression function in Kriging was determined through the correlation coefficient R² and leave one out cross validation (LOOCV). Through selection, crossover and mutation, the optimal individual in each iteration was obtained, and the error between the real value and prediction value by Kriging was calculated. The individual with error larger than the threshold was selected as sequential sample to update the Kriging model. Finally, the optimal axle bridge parameters were obtained. Under the constraints of stress, deformation, and fatigue life, the mass of the axle bridge at the optimal parameters is reduced by 106.5Kg and the mass reduction ratio reaches 22.66%. Therefore, the proposed lightweight method is an effective method to ensure the performance of the axle bridge and reduce the production cost. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 10
        StartPage: 1605
    Subjects:
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Structural optimization
        Type: general
      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Design techniques
        Type: general
      – SubjectFull: Fatigue life
        Type: general
      – SubjectFull: Bridge design & construction
        Type: general
      – SubjectFull: Response surfaces (Statistics)
        Type: general
    Titles:
      – TitleFull: Lightweight Design of Axle Bridge Based on Kriging Model with Optimal Regression.
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            NameFull: Li, Xiaoke
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            NameFull: Du, Haiyang
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            NameFull: Jiang, Qianlong
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            NameFull: Ma, Jun
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            NameFull: Ming, Wuyi
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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            – TitleFull: IAENG International Journal of Computer Science
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