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Record W7028646188

Fatigue Assessment of Bridge Members Based on In-Service Stresses interim report no. 1 August 1996 (FHWA-OK-95-07) 2196

2018· article· en· W7028646188 on OpenAlexaboutno aff

Bibliographic record

VenueSHAREOK (University of Oklahoma) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsWeldingBridge (graph theory)Fatigue testingFatigue limitStress (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Construction problems involving poor welding practice occurred during widening of the U.S. 69 bridge over the South Canadian River. As a result of these problems, cc>ncerns developed regarding the remaining fatigue life of the bridge. A research project was initiated to address these concerns The first portion of the project involved instrumenting the bridge and recording strains under both known loads and normal traffic. These measured strains were used to calibrate an analytical model prepared as a second part of the project. The analytical model was then used to determine the critical location for fatigue. A third part of the project involved conducting laboratory fatigue tests on beam specimens with a welded detail similar to the detail of concern on the bridge. The results of the fatigue tests were used to construct an S-N curve for the detail. Based on the developed S-N curve and stress ranges from computations and measurements, the remaining fatigue life of the bridge is estimated. The estimate indicates that the remaining fatigue life of the bridge is infinite. The reader is cautioned against using the results of this research as justification for poor welding practice. The laboratory tests show that poor welding significantly reduces the fatigue life of a beam. The long remaining life which is estimated for this particular bridge is a result of the low stress ranges in the bridge, which compensate for the inferior quality of the welds. The same result should not be expected in every case.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.000

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 head0.042
GPT teacher head0.294
Teacher spread0.253 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

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