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Full-probabilistic reliability approach for the calibration of traffic load models on highway bridges

 Full-probabilistic reliability approach for the calibration of traffic load models on highway bridges
Auteur(s): ,
Présenté pendant IABSE Congress: Beyond Structural Engineering in a Changing World, San José, Cost Rica, 25-27 Seotember 2024, publié dans , pp. 1119-1127
DOI: 10.2749/sanjose.2024.1119
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This study aims to present an alternative approach to the usual semi-probabilistic approach for the calibration of a traffic load model by considering both action and resistance side of the bridge ...
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Détails bibliographiques

Auteur(s): (ETH Zurich, D-BAUG, Institute of Structural Engineering (IBK), Chair of Steel and Composite Structures, Zurich, Switzerland)
(ETH Zurich, D-BAUG, Institute of Structural Engineering (IBK), Chair of Steel and Composite Structures, Zurich, Switzerland)
Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Congress: Beyond Structural Engineering in a Changing World, San José, Cost Rica, 25-27 Seotember 2024
Publié dans:
Page(s): 1119-1127 Nombre total de pages (du PDF): 9
Page(s): 1119-1127
Nombre total de pages (du PDF): 9
DOI: 10.2749/sanjose.2024.1119
Abstrait:

This study aims to present an alternative approach to the usual semi-probabilistic approach for the calibration of a traffic load model by considering both action and resistance side of the bridge structures. The traffic load models are therefore calibrated by matching the calculated system reliability with the target reliability proposed by the standards by using an optimisation algorithm by including the traffic load sensitivity factors as weight-factors. The presented framework is applied to a selection of simplified example-bridges and axle load sequences based on traffic simulations and is then compared to the semi-probabilistic approach. The proposed workflow highlights additional safeties when considering longer span bridges in bending or composite bridges, but needs special consideration for concrete shear failure modes due to the high assumed resistance model uncertainties.

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