MétaCan
Menu
Back to cohort

Applying the Cyclic Void Growth Model to Assess the Ultralow Cycle Fatigue Life of Steel Castings

2018· article· en· W2807962218 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

VenueJournal of Structural Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsArup Group (Canada)
Fundersnot available
KeywordsVoid (composites)Materials scienceStructural engineeringMetallurgyScale (ratio)Low-cycle fatigueEngineeringComposite material

Abstract

fetched live from OpenAlex

Steel castings have become an attractive option in the development of new seismic force-resisting systems because of their geometric freedom and ability to control material properties. Despite, in most past applications, being used as elements that remain elastic during seismic events, recent research has led to the development and validation of new systems that rely on their yielding response. As such, further study of their ultralow cycle fatigue (ULCF) life is essential to understanding their performance as yielding fuses. Fatigue laws are calibrated herein for multiple heats of cast steel using an existing law proposed for rolled steel. These laws are used to predict the ductile failure of steel castings in full-scale experiments. The concept of a characteristic cyclic coupon test to assess the castings’ ULCF life at the foundry is also discussed. It is found that the ULCF model provides good predictions of the onset of failure in full-scale steel castings. Finally, the initial investigation into relating the full-scale ULCF life to the steel microstructure and small-scale fatigue test results has shown promising results.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.475

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 head0.037
GPT teacher head0.273
Teacher spread0.237 · 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