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Computational methods for model reliability assessment

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  • Rebba, Ramesh
  • Mahadevan, Sankaran

Abstract

This paper investigates various statistical approaches for the validation of computational models when both model prediction and experimental observation have uncertainties, and proposes two new methods for this purpose. The first method utilizes hypothesis testing to accept or reject a model at a desired significance level. Interval-based hypothesis testing is found to be more practically useful for model validation than the commonly used point null hypothesis testing. Both classical and Bayesian approaches are investigated. The second and more direct method formulates model validation as a limit state-based reliability estimation problem. Both simulation-based and analytical methods are presented to compute the model reliability for single or multiple comparisons of the model output and observed data. The proposed methods are illustrated and compared using numerical examples.

Suggested Citation

  • Rebba, Ramesh & Mahadevan, Sankaran, 2008. "Computational methods for model reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1197-1207.
  • Handle: RePEc:eee:reensy:v:93:y:2008:i:8:p:1197-1207
    DOI: 10.1016/j.ress.2007.08.001
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    Citations

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    Cited by:

    1. Vanslette, Kevin & Tohme, Tony & Youcef-Toumi, Kamal, 2020. "A general model validation and testing tool," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    2. Ling, You & Mahadevan, Sankaran, 2013. "Quantitative model validation techniques: New insights," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 217-231.
    3. Sankararaman, Shankar & Mahadevan, Sankaran, 2011. "Model validation under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1232-1241.
    4. Guan, Xuefei & He, Jingjing & Jha, Ratneshwar & Liu, Yongming, 2012. "An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 1-13.
    5. Sankararaman, Shankar & Mahadevan, Sankaran, 2015. "Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 194-209.
    6. Li, Chenzhao & Mahadevan, Sankaran, 2016. "Role of calibration, validation, and relevance in multi-level uncertainty integration," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 32-43.
    7. Kwag, Shinyoung & Gupta, Abhinav & Dinh, Nam, 2018. "Probabilistic risk assessment based model validation method using Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 380-393.
    8. Ao, Dan & Hu, Zhen & Mahadevan, Sankaran, 2017. "Design of validation experiments for life prediction models," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 22-33.
    9. Hund, Lauren & Schroeder, Benjamin & Rumsey, Kellin & Huerta, Gabriel, 2018. "Distinguishing between model- and data-driven inferences for high reliability statistical predictions," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 201-210.
    10. Jiang, Xiaomo & Mahadevan, Sankaran, 2009. "Bayesian structural equation modeling method for hierarchical model validation," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 796-809.
    11. Mullins, Joshua & Ling, You & Mahadevan, Sankaran & Sun, Lin & Strachan, Alejandro, 2016. "Separation of aleatory and epistemic uncertainty in probabilistic model validation," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 49-59.
    12. Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2020. "A generalized Bayesian approach to model calibration," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

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