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Record W2017455402 · doi:10.1115/ipc2006-10537

Methodology to Develop Fitness-for-Service Assessments for Crack Detection In-Line Inspection Data

2006· article· en· W2017455402 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

VenueVolume 2: Integrity Management; Poster Session; Student Paper Competition · 2006
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsReliability engineeringPipeline transportIntegrity managementReliability (semiconductor)Pipeline (software)Service (business)CriticalityEngineeringRisk assessmentComputer scienceRisk analysis (engineering)PrioritizationProcess (computing)Construction engineeringComputer securityMechanical engineering

Abstract

fetched live from OpenAlex

Implementation of Integrity Management Programs (IMP) for pipelines has motivated the design of Fitness-For-Service methodologies to assess Stress Corrosion Cracking (SCC) and fatigue-dependent features reported by Ultrasonic Crack Detection (UTCD) In-Line Inspections. The philosophical approach defined by the API 579 [1] “Fitness-For-Service” from the petrochemical industry in conjunction with Risk-based standards and regulations (i.e. CSA-Z662-2003 [2] and US DOT 49 Parts 192 [3] and 195 [4]) and in-line inspection validation (i.e. API 1163 [5]) approaches from the pipeline industry have provided the engineering basis for ensuring the safety, reliability and continued service of the in-line inspected pipelines. This paper provides a methodology to develop short and long-term excavation and re-inspection programs through a four (4) phase-process: Pre-Assessment, Integrity Criticality Assessment, Remediation and Repair, Remaining Life Extension and In-Service Monitoring. In the first phase, Pre-assessment, areas susceptible to Stress Corrosion Cracking (SCC) and fatigue-dependent features are correlated to in-line inspection data, soil modeling, pipeline and operating conditions, and associated consequences in order to provide a risk-based prioritization of pipeline segments and technical understanding for performing the assessment. The second phase, Integrity Criticality Assessment, will develop a short-term maintenance program based on the remaining strength of the in-line inspection reported features previously correlated, overlaid and risk-ranked. In addition, sites may be identified in Phase 1 for further investigation. In the third phase, a Remediation and Repair program will undertake the field investigation in order to repair and mitigate the potential threats as well as validating the in-line inspection results and characterization made during the Pre-assessment and Integrity Criticality Assessment (Phases 1 & 2). With the acquired knowledge from the previous three (3) phases, a Remaining Life Extension and In-Service Monitoring program will be developed to outline the long-term excavation and re-inspection program through the use of SCC and Fatigue crack growth probabilistic modeling and cost benefit analysis. The support of multiple Canadian and US pipeline operating companies in the development, validation and implementation of this methodology made this contribution possible.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.069
GPT teacher head0.354
Teacher spread0.285 · 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