Decision theoretic approach for identification of optimal proof load with sparse resistance information
(2021)- Abstract
- Proof load testing may be performed to confirm the reliability of the bridge for an existing classification or to prove the reliability for a higher classification.In this paper, a probabilistic decision analysisapproach is applied to the scenario for the evaluation of target proof load in the situation where information on the bridge resistance model is lacking. In this case, the resistance model is established by proof loading and taking very basic prior knowledge into account. The decision scenario is modelled in the context of the proof load test planner who shall choose the required load level for assessment of a bridge. The choice of the load level depends on the risks due to the testing and the expected benefit gain from the test.... (More)
- Proof load testing may be performed to confirm the reliability of the bridge for an existing classification or to prove the reliability for a higher classification.In this paper, a probabilistic decision analysisapproach is applied to the scenario for the evaluation of target proof load in the situation where information on the bridge resistance model is lacking. In this case, the resistance model is established by proof loading and taking very basic prior knowledge into account. The decision scenario is modelled in the context of the proof load test planner who shall choose the required load level for assessment of a bridge. The choice of the load level depends on the risks due to the testing and the expected benefit gain from the test. Information acquired about the loading response from monitoring during the proof load testing is modelled by taking basis in the model uncertainty formulation. The optimal proof load level for classification of a single lane, simply supported bridge of 8m span subjected to live load from very heavy (gross weight > 80 tons) transport vehicles was calculated. The optimal proof load level was identified as leading to a positive expected benefit gain to the decision maker while also satisfying target reliability criteria for remaining service life. The analysis was performed for the evaluation of bridge performance with respect to five classifications of very heavy transport vehicles with different vehicle weights and configurations. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5d7077ea-9d77-4856-be47-84c75364e959
- author
- Kapoor, Medha LU ; Sørensen, John Dalsgaard ; Ghosh, Siddhartha and Thöns, Sebastian LU
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations
- pages
- 8 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85117562872
- ISBN
- 9780429279119
- DOI
- 10.1201/9780429279119-104
- language
- English
- LU publication?
- yes
- id
- 5d7077ea-9d77-4856-be47-84c75364e959
- date added to LUP
- 2021-08-13 17:36:38
- date last changed
- 2022-04-27 03:09:29
@inproceedings{5d7077ea-9d77-4856-be47-84c75364e959,
abstract = {{Proof load testing may be performed to confirm the reliability of the bridge for an existing classification or to prove the reliability for a higher classification.In this paper, a probabilistic decision analysisapproach is applied to the scenario for the evaluation of target proof load in the situation where information on the bridge resistance model is lacking. In this case, the resistance model is established by proof loading and taking very basic prior knowledge into account. The decision scenario is modelled in the context of the proof load test planner who shall choose the required load level for assessment of a bridge. The choice of the load level depends on the risks due to the testing and the expected benefit gain from the test. Information acquired about the loading response from monitoring during the proof load testing is modelled by taking basis in the model uncertainty formulation. The optimal proof load level for classification of a single lane, simply supported bridge of 8m span subjected to live load from very heavy (gross weight > 80 tons) transport vehicles was calculated. The optimal proof load level was identified as leading to a positive expected benefit gain to the decision maker while also satisfying target reliability criteria for remaining service life. The analysis was performed for the evaluation of bridge performance with respect to five classifications of very heavy transport vehicles with different vehicle weights and configurations.}},
author = {{Kapoor, Medha and Sørensen, John Dalsgaard and Ghosh, Siddhartha and Thöns, Sebastian}},
booktitle = {{Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations}},
isbn = {{9780429279119}},
language = {{eng}},
publisher = {{Taylor & Francis}},
title = {{Decision theoretic approach for identification of optimal proof load with sparse resistance information}},
url = {{http://dx.doi.org/10.1201/9780429279119-104}},
doi = {{10.1201/9780429279119-104}},
year = {{2021}},
}