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BMIM Aided Damage Detection in Bridges Using Machine Learning
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.ORCID iD: 0000-0002-5447-2068
2013 (English)Manuscript (preprint) (Other academic)
Place, publisher, year, edition, pages
2013.
National Category
Infrastructure Engineering
Identifiers
URN: urn:nbn:se:kth:diva-150801OAI: oai:DiVA.org:kth-150801DiVA: diva2:745264
Note

QS 2014

Available from: 2014-09-10 Created: 2014-09-10 Last updated: 2014-09-10Bibliographically approved
In thesis
1. Application of monitoring to dynamic characterization and damage detection in bridges
Open this publication in new window or tab >>Application of monitoring to dynamic characterization and damage detection in bridges
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The field of bridge monitoring is one of rapid development. Advances in sensor technologies, in data communication and processing algorithms all affect the possibilities of Structural Monitoring in Bridges. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining their serviceability and deterioration state. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening works. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring.

This thesis consists of an extended summary and five appended papers. The thesis presents advances in sensor technology, damage identification algorithms, Bridge Weigh-In-Motion systems, and other techniques used in bridge monitoring. Four case studies are presented. In the first paper, a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. In the second paper, the seasonal variability of a ballasted railway bridge is studied and characterized in its natural variability. In the third, the non-linear characteristic of a ballasted railway bridge is studied and described stochastically. In the fourth, a novel damage detection algorithm based in Bridge Weigh-In-Motion data and machine learning algorithms is presented and tested on a numerical experiment. In the fifth, a bridge and traffic monitoring system is implemented in a suspension bridge to study the cause of unexpected wear in the bridge bearings.

Some of the major scientific contributions of this work are: 1) the development of a B-WIM for railway traffic capable of estimating the load on individual axles; 2) the characterization of in-situ measured railway traffic in Stockholm, with axle weights and train configuration; 3) the quantification of a hitherto unreported environmental behaviour in ballasted bridges and possible mechanisms for its explanation (this behaviour was shown to be of great importance for monitoring of bridges located in colder climate) 4) the statistical quantification of the nonlinearities of a railway bridge and its yearly variations and 5) the integration of B-WIM data into damage detection techniques.

 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. x, 65 p.
Series
TRITA-BKN. Bulletin, ISSN 1103-4270 ; 126
Keyword
Structural health monitoring, Traffic monitoring, Bridge monitoring, Bridge Weigh-In-Motion, BWIM, Damage detection, Suspension bridge bearings, Axle loads, Dynamics, Temperature effect
National Category
Infrastructure Engineering
Identifiers
urn:nbn:se:kth:diva-150804 (URN)
Public defence
2014-09-19, Sal F3, Lindstedtsvägen 26, Sing-Sing, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20140910

Available from: 2014-09-10 Created: 2014-09-10 Last updated: 2014-09-10Bibliographically approved

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