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Application of monitoring to dynamic characterization and damage detection in bridges
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and 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 [en]
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: urn:nbn:se:kth:diva-150804OAI: oai:DiVA.org:kth-150804DiVA: diva2:745269
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
List of papers
1. Traffic monitoring using a structural health monitoring system
Open this publication in new window or tab >>Traffic monitoring using a structural health monitoring system
2014 (English)In: Proceedings of the ICE - Bridge Engineering, ISSN 1478-4629, Vol. 168, no 1, 13-23 p.Article in journal (Refereed) Published
Abstract [en]

The main factors influencing the deterioration of bridges are the environmental conditions and the traffic loads. Hence, a reliable and accurate characterisation of the traffic loads can improve the results from bridge rating, and health bridge monitoring. In this study a Bridge Weigh-in-Motion algorithm is developed to monitor trains passing on a steel railway bridge. The implemented system estimates the traffic loads, speeds and axle spacings. Other valuable information such as peak and RMS vertical bridge deck accelerations are also stored. The system takes advantage of two of the strain gauges from a previously deployed sensor network, installed to mainly monitor the strains for fatigue development. In this paper the possibilities and limitation of this system are explored.

National Category
Infrastructure Engineering
Identifiers
urn:nbn:se:kth:diva-48707 (URN)10.1680/bren.11.00046 (DOI)2-s2.0-84924428646 (Scopus ID)
Note

QC 20150625

Available from: 2011-11-22 Created: 2011-11-22 Last updated: 2015-06-25Bibliographically approved
2. Seasonal effects on the stiffness properties of a ballasted railway bridge
Open this publication in new window or tab >>Seasonal effects on the stiffness properties of a ballasted railway bridge
2013 (English)In: Engineering structures, ISSN 0141-0296, E-ISSN 1873-7323, Vol. 57, 63-72 p.Article in journal (Refereed) Published
Abstract [en]

In this article it is shown empirically that ballasted bridges in cold climates can exhibit a step-like variation of their natural frequencies as the yearly season changes. The bridge under study was observed to have significantly higher natural frequencies (as much as 35%) during the winter months compared to the summer. This variation was rather discrete in nature and not proportional to temperature. Furthermore the increase in natural frequencies took place only after the temperatures had dropped below 0 °C for a number of days. It was thus hypothesized that this change in natural frequencies was due to changes in the stiffness parameters of some materials with the onset of frost. In low temperature conditions not only the mean value of the measured frequencies increased, but also their variance increased considerably. Given the large spread of the measured natural frequencies, the stiffness parameters were assumed to be stochastic variables with an unknown multivariate distribution, rather than fixed values. A Bayesian updating scheme was implemented to determine this distribution from measurements. Data gathered during one annum of monitoring was used in conjunction with a finite element model and a meta model, resulting in an estimation of the relevant stiffness parameters for both the cold and the warm condition.

Keyword
Railway bridges, Dynamics, Ballasted track, Seasonal effects, Bayesian updating, Markov-Chain Monte-Carlo Sampling
National Category
Infrastructure Engineering
Research subject
Järnvägsgruppen - Infrastruktur
Identifiers
urn:nbn:se:kth:diva-132254 (URN)10.1016/j.engstruct.2013.09.010 (DOI)000330488800006 ()2-s2.0-84885204016 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 286276
Note

QC 20131205

Available from: 2013-10-25 Created: 2013-10-25 Last updated: 2017-12-06Bibliographically approved
3. Analysis of the annual variations in the dynamic behavior of a ballasted railway bridge using Hilbert transform
Open this publication in new window or tab >>Analysis of the annual variations in the dynamic behavior of a ballasted railway bridge using Hilbert transform
2014 (English)In: Engineering structures, ISSN 0141-0296, E-ISSN 1873-7323, Vol. 60, 126-132 p.Article in journal (Refereed) Published
Abstract [en]

In this paper the variations in dynamic properties (eigenfrequency and damping) due to seasonal effects of a single span, ballasted railway bridge are studied. It is demonstrated that both the eigenfrequency and characteristic damping vary importantly with environmental conditions and amplitude of vibration. For this, acceleration signals corresponding to roughly a year of monitoring are analyzed with the Hilbert transform and the instantaneous frequency and equivalent viscous damping ratio are calculated during the free vibrations. Over 1000 trains passages were analyzed, with temperatures ranging from -30 to +30°C and amplitudes of vibration varying from 0.5m/s2 to 0. The location of the accelerometers allowed for separation of the signals into their bending and torsional components. It was found that during the cold season, with months of temperatures below 0°C, the dynamic properties varied the most. Not only did the frequencies (for small vibrations) differ more than 9% even for a given temperature, but the non-linearity present in the structure did also change in a matter of hours. These findings are important in the context of Structural Health Monitoring. Any system that aims at warning early in the onset of damage by analyzing changes in the dynamic characteristic of a structure needs to first fully understand and account for the natural variability of these parameters, often much larger than what could be expected from reasonable levels of damage.

Keyword
Ballasted railway bridge, Hilbert transform, Modal identification, Non-linear, Seasonal effects, Signal analysis
National Category
Construction Management
Identifiers
urn:nbn:se:kth:diva-142976 (URN)10.1016/j.engstruct.2013.12.026 (DOI)000333783400013 ()2-s2.0-84892639665 (Scopus ID)
Note

QC 20140317

Available from: 2014-03-17 Created: 2014-03-14 Last updated: 2017-12-05Bibliographically approved
4. BMIM Aided Damage Detection in Bridges Using Machine Learning
Open this publication in new window or tab >>BMIM Aided Damage Detection in Bridges Using Machine Learning
2013 (English)Manuscript (preprint) (Other academic)
National Category
Infrastructure Engineering
Identifiers
urn:nbn:se:kth:diva-150801 (URN)
Note

QS 2014

Available from: 2014-09-10 Created: 2014-09-10 Last updated: 2014-09-10Bibliographically approved
5. Continuous monitoring of the High Coast Suspension Bridge: Measurement period: February to December 2010
Open this publication in new window or tab >>Continuous monitoring of the High Coast Suspension Bridge: Measurement period: February to December 2010
2011 (English)Report (Other academic)
Publisher
32 p.
Series
Technical Report, 2011:03
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-36805 (URN)
Note
QC 20110809Available from: 2011-08-09 Created: 2011-07-15 Last updated: 2014-09-10Bibliographically approved

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