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Fatigue Behavior of Welded Shear Studs in Precast Composite Beams

2017· article· en· W2752080870 on OpenAlexafffund
Matthew Sjaarda, Taylor Porter, Jeffrey West, Scott Walbridge

Bibliographic record

VenueJournal of Bridge Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Waterloo
FundersMinistère des Transports
KeywordsPrecast concreteStructural engineeringDeckGirderWeldingShear (geology)Beam (structure)EngineeringBridge deckMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Bridges consisting of full-depth precast concrete deck panels connected to steel girders are an increasingly popular option for accelerated bridge replacement. In designing the shear connectors for this bridge type, fatigue is often the governing failure mode. Typically, design provisions developed for cast-in-place deck systems are used. To date, only limited efforts have been undertaken to assess these provisions for precast applications. Most of the data has been obtained from push tests under constant-amplitude loading conditions. Against this background, this article presents measured strain, fatigue life, and autopsy results for 12 composite beam specimens (6 with precast concrete slabs) subjected to a variable-amplitude loading history until multiple stud failures were observed. The results indicate that shear studs in beams with precast slabs exhibit a fatigue performance at least on par with those in beams with cast-in-place slabs. In addition to yielding valuable fatigue data, the test results provide evidence of the effects of redundancy and the value of beam tests, rather than push tests, for assessing and designing shear connectors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.025
GPT teacher head0.264
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations34
Published2017
Admission routes2
Has abstractyes

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