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Record W3125014279 · doi:10.1115/ipc2020-9472

Improved Semi-Quantitative Reliability-Based Method for Assessment of Pipeline Dents With Stress Risers

2020· article· en· W3125014279 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Pipeline (software)Pipeline transportReliability engineeringComputer scienceProcess (computing)Noise (video)Probabilistic logicCurvatureStress (linguistics)EngineeringStructural engineeringMechanical engineeringArtificial intelligenceMathematics

Abstract

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Abstract Dents, especially those interacting with stress risers, can pose integrity threats to pipeline systems. Regulations in Canada and the United States mandate the repair of dents based on depth and interaction with stress risers, however, there have been cases in the past where dents that have passed these criteria have ended up in loss of containment. Recent industry’s recommendations regarding dent integrity analysis are predominantly based on strain, and the dent-fatigue models have been proven to be limited in their application. Additionally, these models or methodologies are generally deterministic which may not fully account for uncertainties associated with pipe properties and in-line inspection (ILI) tool measurement. Enbridge Liquid Pipelines Inc. had previously presented a framework to support system wide dent assessment with an efficient probabilistic-based calibrated semi-quantitative analysis method for dents (SQuAD), which elicits potentially injurious features from thousands of features within a system in a reasonable analysis timeframe. This paper expands on the authors’ previous work and presents several improvements that have since been made to the SQuAD model to address the limitations in the initial version of the model. The previous version of SQuAD was strain-based and did not explicitly account for pressure-cycling induced, fatigue-based failure quantitatively. An approximate circle fitting method was adopted for estimating the dent’s radii of curvature in order to calculate strains. In the improved model, filtering techniques have been employed to reduce the noise in the ILI-reported data while preserving the dent shape. Furthermore, a simplified FEA process has been developed to calculate the stresses within a dent due to pressure cycles, thus the fatigue-based Probability of Failure (PoF) of a dent can now be estimated using S-N approach. The filtered data allows for better accuracy in quantifying the radius of curvature of dents as reported by ILI tools, which are used for calculating dent strain as recommended in the updated version of ASME B31.8, Appendix R. Finally, the feasibility of applying this improved SQuAD model is discussed from an operator’s perspective. The improvements allow the enhanced SQuAD model to be used as an effective screening tool on a system-wide basis as part of a comprehensive, reliability-based dent assessment framework.

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: Methods · Consensus signal: none
Teacher disagreement score0.500
Threshold uncertainty score0.530

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 head0.025
GPT teacher head0.322
Teacher spread0.296 · 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