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Record W2005896418 · doi:10.1115/ipc2004-0525

Application of Constraint-Sensitive Fracture Mechanics to the Assessment of Girth Weld Integrity

2004· article· en· W2005896418 on OpenAlexaff
Yong-Yi Wang, Ming Liu, David Horsley

Bibliographic record

Venue2004 International Pipeline Conference, Volumes 1, 2, and 3 · 2004
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsConstraint (computer-aided design)Fracture mechanicsFracture toughnessFracture (geology)Structural engineeringTension (geology)WeldingBrittlenessMaterials scienceMechanicsComputer scienceEngineeringMechanical engineeringCompression (physics)Composite materialPhysics

Abstract

fetched live from OpenAlex

It has been known since the 1960’s that the crack-tip fields of specimens containing sharp cracks depend on the mode of loading. Experimental evidence has shown that cracked specimens loaded in bending have lower toughness than the same material loaded in tension. This phenomenon has been attributed to the “constraint effect” on fracture. Modern constraint-sensitive fracture mechanics has brought about fundamental theoretical understandings on the constraint effects. The constraint-sensitive fracture mechanics has moved from academic circles to practical application, as evident by a number of recently published fitness-for-service (FFS) assessment procedures that incorporates the constraint effects. This paper reviews the developments in constraint-sensitive fracture mechanics with the focus on its potential applications to the assessment of pipeline girth weld integrity. The review covers (1) constraint theories and experimental evidences, (2) constraint models for brittle and ductile fracture, and (3) limitations of current constraint models. The paper closes with thoughts on the applications of the constraint models and theories to ductile fracture processes, which are the most relevant to modern linepipes and their welds.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.523
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.299
Teacher spread0.283 · 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.

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

Citations1
Published2004
Admission routes1
Has abstractyes

Explore more

Same venue2004 International Pipeline Conference, Volumes 1, 2, and 3Same topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207