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A structural reliability analysis method based on Kriging model with adaptive nested sampling.

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Title: A structural reliability analysis method based on Kriging model with adaptive nested sampling.
Authors: Zhang, Ziyi1 (AUTHOR), Pu, Qianhui1 (AUTHOR) qhpu@vip.163.com
Source: Advances in Structural Engineering. Apr2026, Vol. 29 Issue 6, p1027-1046. 20p.
Subjects: Kriging, Adaptive sampling (Statistics), Monte Carlo method, Sampling methods, Structural engineering, Structural reliability, Prediction models, Structural failures
Abstract: The Kriging model with adaptive sampling is a popular surrogate model for reliability analysis. It selects experimental points based on specific criteria and replaces the performance function for failure probability estimation once the desired accuracy is achieved. However, for small failure probabilities or computationally intensive performance functions, it often exhibits slow convergence due to high computational costs and sample requirements. This paper proposes a structural reliability analysis method based on Kriging Model with Adaptive Nested Sampling (K-ANS). K-ANS refines sampling boundaries iteratively using conditional probability and selects experimental points within subsets through EFF and U learning functions. The Kriging model updates until convergence, and Monte-Carlo Simulation is used for reliability analysis. Validated through two numerical examples and a cable-stayed bridge, K-ANS significantly reduces performance function evaluations while maintaining accuracy, demonstrating its efficiency and applicability to complex engineering structures. Furthermore, the sampling method is versatile and applicable to other surrogate models. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. 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.)
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  Data: A structural reliability analysis method based on Kriging model with adaptive nested sampling.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Ziyi%22">Zhang, Ziyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pu%2C+Qianhui%22">Pu, Qianhui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qhpu@vip.163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Advances+in+Structural+Engineering%22">Advances in Structural Engineering</searchLink>. Apr2026, Vol. 29 Issue 6, p1027-1046. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Kriging%22">Kriging</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+sampling+%28Statistics%29%22">Adaptive sampling (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+methods%22">Sampling methods</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+engineering%22">Structural engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+reliability%22">Structural reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+failures%22">Structural failures</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The Kriging model with adaptive sampling is a popular surrogate model for reliability analysis. It selects experimental points based on specific criteria and replaces the performance function for failure probability estimation once the desired accuracy is achieved. However, for small failure probabilities or computationally intensive performance functions, it often exhibits slow convergence due to high computational costs and sample requirements. This paper proposes a structural reliability analysis method based on Kriging Model with Adaptive Nested Sampling (K-ANS). K-ANS refines sampling boundaries iteratively using conditional probability and selects experimental points within subsets through EFF and U learning functions. The Kriging model updates until convergence, and Monte-Carlo Simulation is used for reliability analysis. Validated through two numerical examples and a cable-stayed bridge, K-ANS significantly reduces performance function evaluations while maintaining accuracy, demonstrating its efficiency and applicability to complex engineering structures. Furthermore, the sampling method is versatile and applicable to other surrogate models. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. 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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        Value: 10.1177/13694332251359324
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        Text: English
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      – SubjectFull: Kriging
        Type: general
      – SubjectFull: Adaptive sampling (Statistics)
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Sampling methods
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      – SubjectFull: Structural engineering
        Type: general
      – SubjectFull: Structural reliability
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      – SubjectFull: Prediction models
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      – SubjectFull: Structural failures
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              M: 04
              Text: Apr2026
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              Y: 2026
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