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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://dirros.openscience.si/IzpisGradiva.php?id=16940"><dc:title>Using statistical analysis of an acceleration-based bridge weigh-in-motion system for damage detection</dc:title><dc:creator>O'Brien,	Eugene J.	(Avtor)
	</dc:creator><dc:creator>Khan,	Muhammad Arslan	(Avtor)
	</dc:creator><dc:creator>McCrum,	Daniel Patrick	(Avtor)
	</dc:creator><dc:creator>Žnidarič,	Aleš	(Avtor)
	</dc:creator><dc:subject>bridge health monitoring</dc:subject><dc:subject>bridge WIM</dc:subject><dc:subject>structural dynamics</dc:subject><dc:subject>damage detection</dc:subject><dc:subject>vehicle-bridge interaction</dc:subject><dc:description>This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles–data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.</dc:description><dc:publisher>MDPI</dc:publisher><dc:date>2020</dc:date><dc:date>2023-09-12 08:31:42</dc:date><dc:type>Neznano</dc:type><dc:identifier>16940</dc:identifier><dc:language>sl</dc:language><dc:rights>
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Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection
by Eugene OBrien
1 [ORCID] , Muhammad Arslan Khan
1,* [ORCID] , Daniel Patrick McCrum
1 [ORCID] and Aleš Žnidarič
2
1
School of Civil Engineering, University College Dublin, D04 V1W8 Belfield, Ireland
2
Slovenian National Building and Civil Engineering Institute (ZAG), 1000 Ljubljana, Slovenia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 663; https://doi.org/10.3390/app10020663
Received: 9 December 2019 / Revised: 13 January 2020 / Accepted: 14 January 2020 / Published: 17 January 2020
(This article belongs to the Section Civil Engineering)
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Abstract
This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analy</dc:rights></rdf:Description></rdf:RDF>
