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Record W1984766745 · doi:10.1061/9780784412367.059

Fatigue Testing and Finite Element Analysis of Bridge Welds Retrofitted by Ultrasonic Impact Treatment

2012· article· en· W1984766745 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

VenueStructures Congress 2012 · 2012
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBridge (graph theory)Scope (computer science)WeldingFinite element methodStructural engineeringEngineeringFatigue testingTest (biology)Computer scienceConstruction engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Ultrasonic impact treatment (UIT) offers a promising means for addressing detected or anticipated fatigue problems in steel highway bridges. Although the beneficial effects of UIT are well documented, a number of issues have slowed its adoption by authorities responsible for highway bridge maintenance. Among the more important of these is the need for simple, quantitative ways to independently verify the quality of the treatment after it is applied. This paper presents the latest results of a research project currently underway with the primary goal of addressing this issue. The scope of this project includes: 1) fatigue tests on welds treated by UIT, 2) experimentation with possible quality control (QC) procedures, and 3) finite element (FE) analysis and fracture mechanics studies conducted with the goal of relating the fatigue test and QC results. This paper discusses the latest fatigue test and FE analysis results, focusing on how characteristics of the treated weld toe, such as the radius or notch depth, can be measured and related to fatigue performance.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.906

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 head0.027
GPT teacher head0.275
Teacher spread0.248 · 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