The use of predictive likelihood to estimate the distribution of extreme bridge traffic load effect
Files in This Item:
|J47 Caprani & OBrien - PL - text 3.pdf||189.13 kB||Adobe PDF||Download|
|Title:||The use of predictive likelihood to estimate the distribution of extreme bridge traffic load effect||Authors:||Caprani, Colin C.
O'Brien, Eugene J.
|Permanent link:||http://hdl.handle.net/10197/2329||Date:||Mar-2010||Abstract:||To assess the safety of an existing bridge, the loads to which it may be subject in its lifetime are required. Statistical analysis is used to extrapolate a sample of load effect values from the simulation period to the required design period. Complex statistical methods are often used and the end result is usually a single value of characteristic load effect. Such a deterministic result is at odds with the underlying stochastic nature of the problem. In this paper, predictive likelihood is shown to be a method by which the distribution of the lifetime extreme load effect may be determined. An estimate of the distributions of lifetime maximum load effect facilitates the reliability approach to bridge assessment. Results are presented for some cases of bridge loading, compared to a return period approach and significant differences identified. The implications for the assessment of existing bridges are discussed.||Funding Details:||European Research Council||Type of material:||Journal Article||Publisher:||Elsevier||Copyright (published version):||2009 Elsevier Ltd||Keywords:||Bridge; Statistics; Load; Predictive likelihood; Probabilistic; Extreme value; Traffic; Monte Carlo; Simulation||Subject LCSH:||Bridges--Live loads
Structural dynamics--Statistical methods
Extreme value theory
|DOI:||10.1016/j.strusafe.2009.09.001||Other versions:||http://dx.doi.org/10.1016/j.strusafe.2009.09.001||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Critical Infrastructure Group Research Collection|
Civil Engineering Research Collection
Show full item record
Page view(s) 10174
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.