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Record W2772283535 · doi:10.2749/222137813806478954

Using Instrumented Quarter-Cars for ‘Drive By’ Bridge Inspection

2013· article· en· W2772283535 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReport · 2013
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of Calgary
FundersScience Foundation Ireland
KeywordsBridge (graph theory)AccelerometerStructural health monitoringStructural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

<p>This paper investigates the concept of ‘drive by’ bridge inspection, a low cost alternative to Structural Health Monitoring (SHM), involving no sensors on the bridge. The concept may be of particular value after an extreme event, such as an earthquake or a flood, where a rapid indication of bridge condition is needed. Vehicle/bridge dynamic interaction is modelled to test the effectiveness of the approach. Damage is simulated here as a change in the bridge damping ratio. Two quarter- cars are simulated crossing the bridge with accelerometers on board. A frequency domain analysis then illustrates changes in the Power Spectral Density of the accelerations as the bridge becomes damaged. The time-lagged difference in the accelerations is found to be effective in detecting damage. Results are compared to those with sensors on the bridge and found to be similar.</p>

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.448

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 head0.035
GPT teacher head0.315
Teacher spread0.280 · 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