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Record W4312442826 · doi:10.1115/ipc2022-86735

Factors Affecting SMAW Pipeline Girth Weld Strength and Strain Concentration Under Tensile Loading

2022· article· en· W4312442826 on OpenAlexaff
Eric Willett, GREG LEHNHOFF

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsWeldingMaterials scienceShielded metal arc weldingGirth (graph theory)Ultimate tensile strengthBendingTensile testingComposite materialStructural engineeringMetallurgyHeat-affected zoneGas metal arc weldingEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract This paper presents the findings of a study to investigate the phenomena of girth weld strength undermatching and strain concentration in SMAW girth welds of API 5L X70 pipe, and to identify the significance of some key factors that may affect these phenomena including filler metal strength, heat input, and carbon equivalency of pipe base material. Test welds were subjected to a novel small-scale instrumented cross-weld tensile testing (ICWT) method employing multiple extensometers in different regions of the gage section. Hardness mapping of each weld cross-section was also performed. This study highlights the importance of considering filler metal strength relative to base material strength when designing welded joints, as well as the importance of controlling welding heat input. It also points out potential issues with relying on failure location as an acceptance criterion in cross-weld tensile testing (CWTT) samples. The observations from this study may be useful in the development of strategies and practices for mitigating the risk of excessive weld strain concentration and premature girth weld failure under moderate tensile or bending loads typical of stress-based pipeline designs.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.037
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0010.000
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 head0.027
GPT teacher head0.264
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueVolume 3: Operations, Monitoring, and Maintenance; Materials and JoiningSame topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207