Probabilistic modelling of bridge safety based on damage indicators

Files in This Item:
File Description SizeFormat 
PROCEDIA_BDB2016_Heitner_rev.pdf656.27 kBAdobe PDFDownload
Title: Probabilistic modelling of bridge safety based on damage indicators
Authors: Heitner, Barbara
O'Brien, Eugene J.
Schoefs, Franck
Yalamas, Thierry
Décatoire, Rodrigue
Leahy, Cathal
Permanent link: http://hdl.handle.net/10197/7997
Date: 1-Oct-2016
Abstract: This paper introduces the various aspects of bridge safety models. It combines the different models of load and resistance involving both deterministic and stochastic variables. The actual safety, i.e. the probability of failure, is calculated using Monte Carlo simulation and accounting for localized damage of the bridge. A possible damage indicator is also presented in the paper and the usefulness of updating the developed bridge safety model, with regards to the damage indicator, is examined.
Funding Details: European Commission Horizon 2020
Type of material: Conference Publication
Publisher: Elsevier
Copyright (published version): 2016 the Authors
Keywords: Reliability;Safety;Probabilistic;Bridges;Bayesian updating;Damage indicator
DOI: 10.1016/j.proeng.2016.08.279
Language: en
Status of Item: Peer reviewed
Conference Details: The 9th International Conference on Bridges in Danube Basin: New Trenda in Bridge Engineering and Efficient Solution for Large and Medium Bridges 2016, Žilina, Slovakia, 30 September - 1 October 2016
Appears in Collections:Civil Engineering Research Collection
TRUSS-ITN Research Collection

Show full item record

SCOPUSTM   
Citations 50

1
Last Week
0
Last month
checked on Jun 15, 2018

Download(s) 50

24
checked on May 25, 2018

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.