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Record W2182227599 · doi:10.71846/12-wcee-0420

DYNAMIC MONITORING OF THE CONFEDERATION BRIDGE

2025· article· en· W2182227599 on OpenAlexaff
David T. Lau, Mo Shing Cheung, Wenchang Li

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsBridge (graph theory)AnemometerStructural health monitoringAccelerometerStrain gaugeVibrationGirderStructural engineeringPrestressed concreteTiltmeterInstrumentation (computer programming)EngineeringAmbient vibrationCalibrationDisplacement (psychology)Vibrating wireSpan (engineering)Wind engineeringMarine engineeringWind speedComputer scienceFinite element methodGeology

Abstract

fetched live from OpenAlex

SUMMARY The Confederation Bridge is one of the world's longest continuous prestressed concrete box girder bridge built over sea water. Because of its location and long span length, the bridge is subjected to significant dynamic loads due to wind, sea current, ice floe impact, earthquakes and heavy vehicles. This paper describes a long-term monitoring program on the dynamic behaviour and performance of the Confederation Bridge. The objectives and scope of the monitoring program, and the design and layout of the instrumentation, are presented. The network of monitoring instruments includes over 100 channels of accelerometers, dynamic tiltmeters, displacement transducers, strain gauges and wind anemometers, to obtain information about the vibrational behaviour of the various components of the bridge. Some ambient vibration data collected as part of the calibration and testing of the monitoring instruments are presented. The field measured data agree well with the results computed using three-dimensional finite element models of the bridge, thus confirming the proper installation and operation of the monitoring equipment. In the study of the dynamic properties of the bridge, the effects of the non-structural components, such as the barrier walls and pavement materials, and the interaction effect of the surrounding water are considered. The monitoring project will not only establish a large comprehensive database on the dynamic behaviour and performance of such a unique concrete structure, but will also provide information for effective maintenance of the facility

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.260
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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