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Record W2088036554 · doi:10.1061/9780784412367.058

Fatigue Testing and Finite Element Analysis of Bridge Welds Retrofitted by Peening under Load

2012· article· en· W2088036554 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueStructures Congress 2012 · 2012
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsUniversity of Waterloo
FundersCanadian Institute of Steel Construction
KeywordsPeeningStructural engineeringResidual stressWeldingFinite element methodHammerMaterials scienceStress (linguistics)Bridge (graph theory)Shot peeningEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Residual stress-based post-weld treatments such as needle peening, hammer peening, and ultrasonic impact treatment (UIT) offer a promising means for extending the fatigue lives of existing welded highway bridges. When applied to bridge welds in service, these treatments can be particularly effective, since the stresses due to the self weight of the bridge have already been imposed. In this paper, a finite element (FE) analysis study is performed to investigate the additional benefit that may result from applying post weld-treatments under load. Fatigue tests of small-scale weld specimens treated with and without preloading are first described. 2D FE models that simulate the treatment process are then described and used to model the treatment of the fatigue specimens. Effects of plate thickness, indentation depth, and preload level on the residual stress distribution induced by peening under load are then studied. Based on the results of this work, recommendations are made to aid in the prediction of the fatigue performance of bridge welds retrofitted by peening under load.

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 score0.063
Threshold uncertainty score0.784

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 head0.030
GPT teacher head0.264
Teacher spread0.234 · 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