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Record W3122302554 · doi:10.1115/ipc2020-9268

Near Neutral pH Stress Corrosion Crack Growth Model Evaluation: PipeOnline™

2020· article· en· W3122302554 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPipeline transportCalibrationCorrosionStress corrosion crackingPipeline (software)Stress (linguistics)Computer scienceFeature (linguistics)Structural engineeringEngineeringMaterials scienceMechanical engineeringMathematicsMetallurgyStatistics

Abstract

fetched live from OpenAlex

Abstract The pipeline industry has long sought a unified near-neutral pH stress corrosion cracking (NNpHSCC) growth model, which fully describes salient growth elements. In response to this gap, the Pipeline Research Council International (PRCI) has funded a multi-year research project, partnering with the University of Alberta (Project SCC-2-12). With the project nearing completion, application of the proposed near-neutral pH stress corrosion cracking growth model to two operating pipelines with known populations of stress corrosion crack features is presented. The remaining life of each crack feature detected by crack in-line inspection tools, under known loading, is calculated for two segments of operating pipelines in North America. The PRCI developed model, referred to as PipeOnline™, is compared to the legacy Enbridge linear growth and Paris Law models. A calibration technique for correcting the length and depth of the ILI feature calls provided by the in-line inspection vendor is reviewed, which takes into account tool tolerance and corrects length and depth to more closely match field findings. Efficiency improvements gleaned from this calibration technique are illustrated. While this calibration methodology is unique to the pipeline operator, the method is reviewed to allow other operators to readily implement the technique if it is found to be warranted. The PipeOnline model is tested for the post-calibration dimensions and compared to the legacy growth model. Each of the required inputs is defined, and methods of quantification are shown. Negligible growth thresholds are reviewed, and the truncation of stress cycles below the growth threshold is discussed. The strategy of deployment is shown, along with the proportion of features that are predicted to remain in dormancy. Methods to account for mean stresses and load application frequency are presented. The resulting PipeOnline re-inspection interval is compared to that predicted by typical existing growth models and then contrasted with excavation results on the asset. Calibration of the governing equation coefficients with rationale for each term is proposed for the pipeline segments examined in the study, and recommendations made for potential implementation for other operators, along with follow-on research.

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 score0.054
Threshold uncertainty score0.765

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)0.0010.000

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 head0.031
GPT teacher head0.252
Teacher spread0.222 · 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