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The latent failure probability: A conceptual basis for robust, reliability-based and risk-based design optimization

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  • Beck, André T.
  • Rodrigues da Silva, Lucas A.
  • Miguel, Leandro F.F.

Abstract

The safety of structural systems depends on social, political, financial, organizational, behavioral, and other non-structural factors. These factors are related to use of the structure and its environment, malevolent and other unanticipated accidental loading, construction method and experience of construction crews, gross errors, monitoring to prevent abuse, etc. The above can broadly be classified as non-technical factors, involving significant epistemic uncertainty. In this manuscript, we formalize a procedure to handle non-technical factors and/or epistemic uncertainties in Robust, Reliability-based and Risk-based structural Design Optimization (R3DO). We propose that latent failure probabilities, subjective point-estimates reflecting non-technical factors and/or epistemic uncertainties, be added to the nominal member failure probabilities, calculated from objective aleatory uncertainty (technical factors). Latent failure probabilities are subjectively estimated by the analysis team, based on a holistic risk analysis addressing the technical and non-technical factors above. The relevance and impact of latent failure probabilities in R3DO have already been demonstrated elsewhere; these results are briefly recapped herein. We demonstrate that structural systems should be made redundant, not because of objective aleatory uncertainties in loads and material strengths, but to cope with the large impact of subjective epistemic uncertainties related to non-technical factors.

Suggested Citation

  • Beck, André T. & Rodrigues da Silva, Lucas A. & Miguel, Leandro F.F., 2023. "The latent failure probability: A conceptual basis for robust, reliability-based and risk-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
  • Handle: RePEc:eee:reensy:v:233:y:2023:i:c:s095183202300042x
    DOI: 10.1016/j.ress.2023.109127
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    References listed on IDEAS

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    1. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    2. Fiondella, Lance & Xing, Liudong, 2015. "Discrete and continuous reliability models for systems with identically distributed correlated components," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 1-10.
    3. Liu, Mingli & Wang, Dan & Zhao, Jiangbin & Si, Shubin, 2022. "Importance measure construction and solving algorithm oriented to the cost-constrained reliability optimization model," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
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    1. Ajenjo, Antoine & Ardillon, Emmanuel & Chabridon, Vincent & Cogan, Scott & Sadoulet-Reboul, Emeline, 2023. "Robustness evaluation of the reliability of penstocks combining line sampling and neural networks," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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