Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation.

An important segment of the reliability-based optimization problems is to get access to the sensitivity derivatives. However, since the failure probability is not a closed-form function of the input variables, the derivatives are not explicitly computable and rather require a full reliability analys...

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Main Authors: Mahdi Shadab Far, Hongwei Huang
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0213199
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spelling doaj-2c3dd64924d14f968a4319963bdcd5902021-03-03T19:46:16ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01143e021319910.1371/journal.pone.0213199Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation.Mahdi Shadab FarHongwei HuangAn important segment of the reliability-based optimization problems is to get access to the sensitivity derivatives. However, since the failure probability is not a closed-form function of the input variables, the derivatives are not explicitly computable and rather require a full reliability analysis which is computationally expensive. In this paper, a step-by-step algorithm has been presented to calculate the derivatives of the probability of failure and safety index with respect to the input parameters based on the advanced first-order second-moment (AFOSM) reliability method. The proposed algorithm is then implemented in a spreadsheet using Visual Basic for Application (VBA) programming language. Two geotechnical and structural examples are then presented to examine the program and describe the modeling procedure. The robustness of the proposed method is examined using a Gaussian random perturbation. The capability of the proposed method in the calculation of the sensitivity derivatives of the model uncertainty is explained in a separate section. Finally, the proposed model has been compared to the forward finite difference (FFD) method and the results are validated.https://doi.org/10.1371/journal.pone.0213199
collection DOAJ
language English
format Article
sources DOAJ
author Mahdi Shadab Far
Hongwei Huang
spellingShingle Mahdi Shadab Far
Hongwei Huang
Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation.
PLoS ONE
author_facet Mahdi Shadab Far
Hongwei Huang
author_sort Mahdi Shadab Far
title Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation.
title_short Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation.
title_full Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation.
title_fullStr Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation.
title_full_unstemmed Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation.
title_sort simplified algorithm for reliability sensitivity analysis of structures: a spreadsheet implementation.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2019-01-01
description An important segment of the reliability-based optimization problems is to get access to the sensitivity derivatives. However, since the failure probability is not a closed-form function of the input variables, the derivatives are not explicitly computable and rather require a full reliability analysis which is computationally expensive. In this paper, a step-by-step algorithm has been presented to calculate the derivatives of the probability of failure and safety index with respect to the input parameters based on the advanced first-order second-moment (AFOSM) reliability method. The proposed algorithm is then implemented in a spreadsheet using Visual Basic for Application (VBA) programming language. Two geotechnical and structural examples are then presented to examine the program and describe the modeling procedure. The robustness of the proposed method is examined using a Gaussian random perturbation. The capability of the proposed method in the calculation of the sensitivity derivatives of the model uncertainty is explained in a separate section. Finally, the proposed model has been compared to the forward finite difference (FFD) method and the results are validated.
url https://doi.org/10.1371/journal.pone.0213199
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