A Bayesian approach for estimating characteristic bridge traffic load effects

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Title: A Bayesian approach for estimating characteristic bridge traffic load effects
Authors: Enright, Bernard
Leahy, Cathal
O'Brien, Eugene J.
Permanent link: http://hdl.handle.net/10197/7072
Date: Aug-2014
Abstract: This paper investigates the use of Bayesian updating to improve estimates of characteristic bridge traffic loading. Over recent years the use Weigh-In-Motion technologies has increased hugely. Large Weigh-In-Motion databases are now available for multiple sites on many road networks. The objective of this work is to use data gathered throughout a road network to improve site-specific estimates of bridge loading at a specific Weigh-In-Motion site on the network. Bayesian updating is a mathematical framework for combining prior knowledge with new sample data. The approach is applied here to bridge loading using a database of 81.6 million truck records, gathered at 19 sites in the US. The database represents the prior knowledge of loading throughout the road network and a new site on the network is simulated. The Bayesian approach is compared with a non-Bayesian approach, which uses only the site-specific data, and the results compared. It is found that the Bayesian approach significantly improves the accuracy of estimates of 75-year loading and, in particular, considerably reduces the standard deviation of the error. With the proposed approach less site-specific WIM data is required to obtain an accurate estimate of loading. This is particularly useful where there is concern over an existing bridge and accurate estimates of loading are required as a matter of urgency.
Type of material: Conference Publication
Copyright (published version): 2014 the Authors
Keywords: BayesianUpdatingBridgeCharacteristicTrafficLoadModelling
Other versions: http://www.cerai.net/
Language: en
Status of Item: Peer reviewed
Conference Details: Civil Engineering Research in Ireland, Belfast, UK, 28 -29 August, 2014
Appears in Collections:Civil Engineering Research Collection

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