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Record W2556897566

Analyzing the Dynamic Behavior of Suspension Bridge Towers Using GPS

2006· article· en· W2556897566 on OpenAlexaboutno aff
Ana Paula Camargo Larocca, Ricardo Ernesto Schaal, Marcelo C. Santos, Richard B. Langley

Bibliographic record

VenueProceedings of the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2006) · 2006
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)TowerSuspension (topology)Global Positioning SystemDeckStructural engineeringGeodesyBeam (structure)EngineeringGeologyTelecommunicationsMathematics
DOInot available

Abstract

fetched live from OpenAlex

The aim of this work is to characterize the dynamic oscillation of the top of the towers of a suspension bridge with GPS and to analyze the resulting values by Fourier analysis and wavelet transform. It is a complementary research about the analysis of the dynamic movements of the Pierre-Laporte Suspension Bridge in Quebec City, Canada. A previous work [Larocca et al., 2005b] analyzed the deck’s movements of this bridge. Suspension bridge fundamentally consists of cables anchored to the earth at their ends and supported by towers at intermediate points. From these cables, a floor or 'deck' is suspended. Therefore, the towers have to be flexible enough to allow for changes in length due to live loads and temperature. Theoretically, the tower can be assumed as a thin beam. GPS data were collected at the towers of the bridge. The data sets were collected by researchers from the Centre de Recherche en Geomatique at Universite Laval in July 1996. One GPS receiver was installed on the top of each of the towers, both 110 m in height, whereas a third receiver was placed on the ground, used as reference. Two 3-hour GPS sessions with a data-sampling interval of 2 seconds were collected. As no other sensors were used for measuring the deflections, the conclusions about the results are supported by theoretical values.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.018
GPT teacher head0.294
Teacher spread0.275 · 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 designBench or experimental
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

Citations4
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2006)Same topicStructural Health Monitoring TechniquesFrench-language works237,207