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Spatially Variable Assessment of Lifetime Maximum Load Effect Distribution in Bridges
Date Issued
2012-09-07
Date Available
2015-09-22T09:49:58Z
Abstract
Bridge structures are key components of highway infrastructure and their safety is clearly of great importance. Safety assessment of highway bridges requires accurate prediction of the extreme load effects, taking account of spatial variability through the bridge width and length. This concept of spatial variability i s also known as random field analysis. Reliability - based bridge assessment permits the inclusion of uncertainty in all parameters and models associated with the deterioration process. Random field analysis takes account of the probability that two points n ear each other on a bridge will have correlated properties. This method incorporates spatial variability which results in a more accurate reliability assessm ent. This paper presents an integrated model for spatial reliability analysis of reinforced concre te bridges that considers both the bridge capacity and traffic load. A sophisticated simulation model of two - directional traffic is used to determine accurate annual maximum distributions of load effect. To generate the bridge loading scenarios, an extensi ve Weigh-in-Motion (WIM) database, from five European countries, is used. For this, statistical distributions for vehicle weights, inter - vehicle gaps and other characteristics are derived from the measurements, and are used as the basis for a Monte Carlo simulation of traffic. Results are presented for bidirectional traffic, with one lane in each direction, with a total flow of approximately 2000 trucks per day.
Type of Material
Conference Publication
Copyright (Published Version)
2012 the Authors
Web versions
Language
English
Status of Item
Peer reviewed
Conference Details
Bridge and Concrete Research in Dublin, Ireland, 6 - 7 September, 2012
This item is made available under a Creative Commons License
File(s)
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Name
C137.pdf
Size
637.94 KB
Format
Adobe PDF
Checksum (MD5)
274778bc2a5a4493283c180ff77a1874
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