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  5. Statistical computation for extreme bridge traffic load effects
 
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Statistical computation for extreme bridge traffic load effects

Author(s)
Caprani, Colin C.  
O'Brien, Eugene J.  
Uri
http://hdl.handle.net/10197/4142
Date Issued
2006-09-12
Date Available
2013-02-26T15:05:33Z
Abstract
The maintenance of highway infrastructure constitutes a major expenditure in many
countries. This cost can be reduced significantly by minimizing the repair or
replacement of highway bridges. In the assessment of existing bridges, the strength
estimate tends to be more accurate than that of traffic loading, due to the more
variable nature of loading. Recent advances in the statistical analysis of highway
bridge traffic loading have resulted in more accurate forecasts of the actual loading
to which a bridge is subject. While these advances require extensive numerical
computation, they can significantly improve the accuracy of the calculation. This
paper outlines the recent advances and describes the associated computational
aspects in detail
Type of Material
Conference Publication
Publisher
Civil-Comp
Copyright (Published Version)
2006, Civil-Comp Ltd.
Subjects

Bridge

Statistics

Loading

Simulation

Predictive likelihood...

Traffic

Web versions
http://www.ctresources.info/ccp/download/ccp.5237.pdf
Language
English
Status of Item
Not peer reviewed
Journal
B.H.V. Topping, G. Montero and R. Montenegro (eds.). Proceedings of the Eighth International Conference on Computational Structures Technology
Conference Details
Eighth International Conference on Computational Structures Technology, Las Palmas de Gran Canaria, Spain, from 12 to 15 September 2006
ISBN
9781905088065
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
File(s)
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Thumbnail Image
Name

c80.pdf

Size

185.95 KB

Format

Adobe PDF

Checksum (MD5)

525e0bbdfd2f845f1867997d11ae1db3

Owning collection
Civil Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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