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Record W2128153051 · doi:10.1061/9780784412374.014

Multimetric Monitoring of a Historic Swing Bridge

2012· article· en· W2128153051 on OpenAlexaff
Ryan K. Giles, Robin Kim, Steven C. Sweeney, Bill Spencer, Lawrence A. Bergman, Carol K. Shield, Steve Olson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsStuart Olson (Canada)
Fundersnot available
KeywordsSwingBridge (graph theory)Structural health monitoringSpan (engineering)Wireless sensor networkComputer scienceTelecommunicationsEngineeringStructural engineeringMechanical engineeringComputer network

Abstract

fetched live from OpenAlex

The Rock Island Arsenal Government Bridge, built over the Mississippi River in 1896 between Rock Island, IL and Davenport, IA, is just one of over two hundred bridges owned by the United States Army. The swing span of the Rock Island Arsenal Government Bridge has the ability to rotate 360° in either direction and can lock each end of the span on either abutment. The bridge carries highway and rail traffic; the swing span allows for the passage of barge traffic on the river. The Army regularly inspects and maintains their bridges to ensure their functionality. To supplement the regular inspections of the swing span of the Government Bridge, the US Army Engineering Research and Development Center (ERDC) has installed a structural health monitoring system composed of both a fiber optic sensor network and a wireless smart sensor network. This multi-framework system measures strain, acceleration, and orientation and uses these metrics to perform structural health monitoring of the bridge. The monitoring is designed to automatically record the measured changes in strain caused by swing events and record the accelerations measured during train events and other scheduled times of day. The integrated monitoring system has been successful in recording the necessary multimetric sensor data and using it to monitor the swing span of the Government Bridge.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.424

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 head0.045
GPT teacher head0.302
Teacher spread0.257 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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