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Bridge health monitoring system based on vibration measurements

Bridge health monitoring system based on vibration measurements
Bridge health monitoring system based on vibration measurements
A bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors. Sophisticated structural identification methods, combining information from the sensor network with the theoretical information built into a finite element model for simulating bridge behaviour, are incorporated into the system in order to monitor structural condition, track structural changes and identify the location, type and extent of damage. This work starts with a brief overview of the modal and model identification algorithms and software incorporated into the monitoring system and then presents details on a Bayesian inference framework for the identification of the location and the severity of damage using measured modal characteristics. The methodology for damage detection combines the information contained in a set of measurement modal data with the information provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in the structure. The effectiveness of the damage detection algorithm is demonstrated and validated using simulated modal data from an instrumented R/C bridge of the Egnatia Odos motorway, as well as using experimental vibration data from a laboratory small-scaled bridge section.
structural monitoring, model updating, Bayesian inference, structural identification, damage detection
1570-761X
469-483
Ntotsios, Evangelos
877c3350-0497-4471-aa97-c101df72e05e
Papadimitriou, Costas
3be78708-ed90-4a1f-b18e-5fe4ec2c8de6
Panetsos, Panagiotis
9da8645c-ca08-4db4-a110-3cdc8d6cdd84
Karaiskos, Grigorios
c2190c00-4a03-41b0-971f-ca1450514d49
Perros, Kyriakos
fb2d568a-a430-4b81-9a41-3ca36f3ca93d
Perdikaris, Filippos
5d09cc65-82c5-414f-97d0-43579b27f0cf
Ntotsios, Evangelos
877c3350-0497-4471-aa97-c101df72e05e
Papadimitriou, Costas
3be78708-ed90-4a1f-b18e-5fe4ec2c8de6
Panetsos, Panagiotis
9da8645c-ca08-4db4-a110-3cdc8d6cdd84
Karaiskos, Grigorios
c2190c00-4a03-41b0-971f-ca1450514d49
Perros, Kyriakos
fb2d568a-a430-4b81-9a41-3ca36f3ca93d
Perdikaris, Filippos
5d09cc65-82c5-414f-97d0-43579b27f0cf

Ntotsios, Evangelos, Papadimitriou, Costas, Panetsos, Panagiotis, Karaiskos, Grigorios, Perros, Kyriakos and Perdikaris, Filippos (2009) Bridge health monitoring system based on vibration measurements. Bulletin of Earthquake Engineering, 7 (2), 469-483. (doi:10.1007/s10518-008-9067-4).

Record type: Article

Abstract

A bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors. Sophisticated structural identification methods, combining information from the sensor network with the theoretical information built into a finite element model for simulating bridge behaviour, are incorporated into the system in order to monitor structural condition, track structural changes and identify the location, type and extent of damage. This work starts with a brief overview of the modal and model identification algorithms and software incorporated into the monitoring system and then presents details on a Bayesian inference framework for the identification of the location and the severity of damage using measured modal characteristics. The methodology for damage detection combines the information contained in a set of measurement modal data with the information provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in the structure. The effectiveness of the damage detection algorithm is demonstrated and validated using simulated modal data from an instrumented R/C bridge of the Egnatia Odos motorway, as well as using experimental vibration data from a laboratory small-scaled bridge section.

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More information

Published date: 22 July 2009
Keywords: structural monitoring, model updating, Bayesian inference, structural identification, damage detection
Organisations: Dynamics Group

Identifiers

Local EPrints ID: 372218
URI: https://eprints.soton.ac.uk/id/eprint/372218
ISSN: 1570-761X
PURE UUID: 6d1b37da-718f-4d44-88b1-c44daf13422a
ORCID for Evangelos Ntotsios: ORCID iD orcid.org/0000-0001-7382-0948

Catalogue record

Date deposited: 04 Dec 2014 13:51
Last modified: 20 Jul 2019 00:38

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