Application of the Rosenblatt transformation in First-Order System Reliability approximations

The reliability assessment of structural systems presents a significant challenge in structural engineering. A commonly employed approximation is the First-Order System Reliability Method (FOSRM), which estimates system reliability using the FORM component reliabilities and sensitivity factors. An essential step in FORM involves transforming the random vector X into the standard vector U, often using the Rosenblatt transformation (RT). Several studies demonstrated that different conditioning orders in the RT yield different FORM component results. This study investigates how these differences on component level propagate into the FOSRM system level. We conducted several typical engineering case studies with various failure probabilities, system sizes, and dependency structures (Gaussian and Frank Copula). For the Frank Copula, different Rosenblatt conditioning orders systematically yielded different FOSRM results, with most cases showing differences between 10% and 30% in estimated failure probability. For some systems, these differences increased with system size, suggesting that greater variations might be observed for larger systems. Notably, systems with Gaussian Copula functions also proved vulnerable to the Rosenblatt conditioning order when different components were assessed with different conditioning orders. The observed differences were larger than previously reported and should be carefully considered in uniform safety assessments.

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Publication Details

Journal
Structural Safety
Published
2024-08-19
DOI
https://doi.org/10.1016/j.strusafe.2024.102521
Citations
8
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
3.69

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article

Application of the Rosenblatt transformation in First-Order System Reliability approximations

N. E. Meinen, R.D.J.M. Steenbergen
8 citations
Structural Safety
Probabilistic and Robust Engineering Design
3.69
article

Application of the Rosenblatt transformation in First-Order System Reliability approximations

N. E. Meinen, R.D.J.M. Steenbergen
article en
8 citations

Abstract

The reliability assessment of structural systems presents a significant challenge in structural engineering. A commonly employed approximation is the First-Order System Reliability Method (FOSRM), which estimates system reliability using the FORM component reliabilities and sensitivity factors. An essential step in FORM involves transforming the random vector X into the standard vector U, often using the Rosenblatt transformation (RT). Several studies demonstrated that different conditioning orders in the RT yield different FORM component results. This study investigates how these differences on component level propagate into the FOSRM system level. We conducted several typical engineering case studies with various failure probabilities, system sizes, and dependency structures (Gaussian and Frank Copula). For the Frank Copula, different Rosenblatt conditioning orders systematically yielded different FOSRM results, with most cases showing differences between 10% and 30% in estimated failure probability. For some systems, these differences increased with system size, suggesting that greater variations might be observed for larger systems. Notably, systems with Gaussian Copula functions also proved vulnerable to the Rosenblatt conditioning order when different components were assessed with different conditioning orders. The observed differences were larger than previously reported and should be carefully considered in uniform safety assessments.

Structural SafetyVol. 112
Netherlands Organisation for Applied Scientific Research (NL), University of Southern Denmark (DK), Ghent University (BE)
Ministry of Infrastructure and Water Management, Rijkswaterstaat
Openalex Percentile: Top 7%
Probabilistic and Robust Engineering Design
3.69
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Application of the Rosenblatt transformation in First-Order System Reliability approximations — N. E. Meinen, R.D.J.M. Steenbergen · Structural Safety (2024) | TGRS Research Map | TGRS