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Record W2899809511 · doi:10.1115/ipc2018-78016

State-of-the-Art Assessment of Today’s Composite Repair Technologies

2018· article· en· W2899809511 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

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsPipeline (software)WeldingEngineeringScale (ratio)Construction engineeringComputer scienceEngineering managementManufacturing engineeringMechanical engineering

Abstract

fetched live from OpenAlex

For almost 30 years composite repair technologies have been used to reinforce high pressure gas and liquid pipeline transmission systems around the world. The backbone of this research has been full-scale testing, aimed at evaluating the reinforcement of anomalies including, corrosion, dents, vintage girth welds, and wrinkle bends. Also included have been the assessment of reinforced pipe geometries including welded branch connections, elbows, and tees. Organizations sponsoring these research efforts have included the Pipeline Research Council International, regulatory agencies, pipeline operators, and composite repair manufacturers. Many of these efforts have involved Joint Industry Programs; to date more than 15 different industry-sponsored programs and independent research efforts have been conducted involving more than 1,000 full-scale destructive tests. The aim of this paper is to provide for the pipeline industry an updated perspective on research associated with composite repair technologies. Because of the continuous advance in both composite technology and research programs to evaluate their effectiveness, it is essential that updated information be provided to industry to minimize the likelihood for conducting research efforts that have already been addressed. To provide readers with useful information, the authors will include multiple case studies that include the reinforcement of dents, wrinkle bends, welded branch connections, and planar defects.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.380

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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