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Record W3134338620 · doi:10.2749/newyork.2019.1398

Prediction of Bearing Lifetime Demands by Considering the Bridge Design and Location Parameters

2019· article· en· W3134338620 on OpenAlex
Minesh K. Patel, Georgios P. Balomenos

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 · 2019
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBearing (navigation)Bridge (graph theory)Span (engineering)Structural engineeringEngineeringBearing capacityGirderTerm (time)Computer scienceEnvironmental science

Abstract

fetched live from OpenAlex

<p>The<span>long-term performance and safety of bridges is of paramount importance. Researchers have placed significant focus on the degradation and deterioration of bridge materials such as steel and concrete, but significantly less is known about the long-term behavior of bridge bearings. Uncertainty in the bearing behavior over time leads to challenges about when the bearings should be inspected and potentially replaced. However, bearing demands vary greatly based on the design of the bridge (e.g. differences in bridge material, girder type, span, height, and location). This paper finds trends in lifetime bearing demands from seismic, thermal, and traffic loading when the bridge design and location parameters are considered. These results can be used to identify which of the parameters have the greatest influence on the lifetime bearing demands which can then be used, in turn, to evaluate bearing long-term performance.</span></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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.183

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 head0.026
GPT teacher head0.214
Teacher spread0.189 · 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