Modification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures

In complex engineering models, various uncertain parameters affect the computational results. Most of them can only be estimated or assumed quite generally. In such a context, measurements are interesting to determine the most decisive parameters accurately. While measurements can reduce parameters’...

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Autores principales:	David Sanio, Mark Alexander Ahrens, Peter Mark
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sensitivity analysis probability probabilistic methods Monte Carlo monitoring lifetime prediction Technology T
Acceso en línea:	https://doaj.org/article/4b1a78b9376241938e9bf81d16d0c2aa
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spelling	oai:doaj.org-article:4b1a78b9376241938e9bf81d16d0c2aa2021-11-25T17:58:48ZModification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures10.3390/infrastructures61101492412-3811https://doaj.org/article/4b1a78b9376241938e9bf81d16d0c2aa2021-10-01T00:00:00Zhttps://www.mdpi.com/2412-3811/6/11/149https://doaj.org/toc/2412-3811In complex engineering models, various uncertain parameters affect the computational results. Most of them can only be estimated or assumed quite generally. In such a context, measurements are interesting to determine the most decisive parameters accurately. While measurements can reduce parameters’ variance, structural monitoring might improve general assumptions on distributions and their characteristics. The decision on variables being measured often relies on experts’ practical experience. This paper introduces a method to stochastically estimate the potential benefits of measurements by modified sensitivity indices. They extend the established variance-based sensitivity indices originally suggested by Sobol’. They do not quantify the importance of a variable but the importance of its variance reduction. The numerical computation is presented and exemplified on a reference structure, a 50-year-old pre-stressed concrete bridge in Germany, where the prediction of the fatigue lifetime of the pre-stressing steel is of concern. Sensitivity evaluation yields six important parameters (e.g., shape of the <i>S–N</i> curve, temperature loads, creep, and shrinkage). However, taking into account individual monitoring measures and suited measurements identified by the modified sensitivity indices, creep and shrinkage, temperature loads, and the residual pre-strain of the tendons turn out to be most efficient. They grant the highest gains of accuracy with respect to the lifetime prediction.David SanioMark Alexander AhrensPeter MarkMDPI AGarticlesensitivity analysisprobabilityprobabilistic methodsMonte Carlomonitoringlifetime predictionTechnologyTENInfrastructures, Vol 6, Iss 149, p 149 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sensitivity analysis probability probabilistic methods Monte Carlo monitoring lifetime prediction Technology T
spellingShingle	sensitivity analysis probability probabilistic methods Monte Carlo monitoring lifetime prediction Technology T David Sanio Mark Alexander Ahrens Peter Mark Modification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures
description	In complex engineering models, various uncertain parameters affect the computational results. Most of them can only be estimated or assumed quite generally. In such a context, measurements are interesting to determine the most decisive parameters accurately. While measurements can reduce parameters’ variance, structural monitoring might improve general assumptions on distributions and their characteristics. The decision on variables being measured often relies on experts’ practical experience. This paper introduces a method to stochastically estimate the potential benefits of measurements by modified sensitivity indices. They extend the established variance-based sensitivity indices originally suggested by Sobol’. They do not quantify the importance of a variable but the importance of its variance reduction. The numerical computation is presented and exemplified on a reference structure, a 50-year-old pre-stressed concrete bridge in Germany, where the prediction of the fatigue lifetime of the pre-stressing steel is of concern. Sensitivity evaluation yields six important parameters (e.g., shape of the <i>S–N</i> curve, temperature loads, creep, and shrinkage). However, taking into account individual monitoring measures and suited measurements identified by the modified sensitivity indices, creep and shrinkage, temperature loads, and the residual pre-strain of the tendons turn out to be most efficient. They grant the highest gains of accuracy with respect to the lifetime prediction.
format	article
author	David Sanio Mark Alexander Ahrens Peter Mark
author_facet	David Sanio Mark Alexander Ahrens Peter Mark
author_sort	David Sanio
title	Modification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures
title_short	Modification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures
title_full	Modification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures
title_fullStr	Modification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures
title_full_unstemmed	Modification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures
title_sort	modification of variance-based sensitivity indices for stochastic evaluation of monitoring measures
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4b1a78b9376241938e9bf81d16d0c2aa
work_keys_str_mv	AT davidsanio modificationofvariancebasedsensitivityindicesforstochasticevaluationofmonitoringmeasures AT markalexanderahrens modificationofvariancebasedsensitivityindicesforstochasticevaluationofmonitoringmeasures AT petermark modificationofvariancebasedsensitivityindicesforstochasticevaluationofmonitoringmeasures
_version_	1718411739731591168

Modification of Variance-Based Sensitivity Indices for Stochastic Evaluation of Monitoring Measures

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