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Uncertainty in condition prediction of bridges based on assessment method – case study in Estonia

 Uncertainty in condition prediction of bridges based on assessment method – case study in Estonia
Auteur(s): , , ORCID
Présenté pendant IABSE Symposium: Towards a Resilient Built Environment Risk and Asset Management, Guimarães, Portugal, 27-29 March 2019, publié dans , pp. 1758-1765
DOI: 10.2749/guimaraes.2019.1758
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In this paper the uncertainty in condition assessment based on most common assessment methods, visual inspection and non-destructive testing, is investigated. For decision-making the averaged or es...
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Détails bibliographiques

Auteur(s): (Tallinn University of Technology, Tallinn, Estonia)
(Tallinn University of Technology, Tallinn, Estonia)
ORCID (University of Minho, Guimarães, Portugal)
Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Symposium: Towards a Resilient Built Environment Risk and Asset Management, Guimarães, Portugal, 27-29 March 2019
Publié dans:
Page(s): 1758-1765 Nombre total de pages (du PDF): 8
Page(s): 1758-1765
Nombre total de pages (du PDF): 8
DOI: 10.2749/guimaraes.2019.1758
Abstrait:

In this paper the uncertainty in condition assessment based on most common assessment methods, visual inspection and non-destructive testing, is investigated. For decision-making the averaged or estimated value is suitable, but if the basis of a decision is only a subjective visual inspection, then it could lead to a wrong decision. The second most traditional assessment method is non-destructive testing (NDT), which can give reliable results, but the interpretation of measurement is needed. To investigate the errors in both evaluations, benchmarking tests were carried out in Estonia within two groups, a group of experienced inspectors and a group of unexperienced students, to show how the importance of experience affects results. To present the influence of assessment uncertainty to condition prediction curves based on continuous-time Markov model are calculated and for updating, Bayesian inference procedure is used.