Methodology to Develop Fitness-for-Service Assessments for Crack Detection In-Line Inspection Data
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".