Limit state-based fitness-for-service assessments of steel pipelines containing dent-gouges
Bibliographic record
Abstract
This study presents a framework for the limit state-based assessment (LSBA) of steel pipelines containing damages in the form of dent-gouges. The LSBA is formulated based on the factored burst capacity computed using the improved European Pipeline Research Group model for dent-gouges with partial safety factors assigned to key input variables and factored pipeline internal pressure. A novel methodology is developed to calibrate the partial safety factors by making the outcomes of LSBA consistent with those of the reliability-based assessment for a set of assessment cases representative of in-service pipelines. The first-order reliability method is employed to evaluate the failure probabilities of the assessment cases. The validity of the calibrated partial safety factors is demonstrated based on a large set of assessment cases that are independent of those employed in the calibration process. The advantages of LSBA over the deterministic fitness-for-service assessment are further illustrated. The proposed framework for LSBA can be applied to pipelines containing other types of damages such as corrosion and cracks, and will facilitate the performance-based pipeline integrity management practice. • A limit state-based assessment framework for dent-gouged pipelines. • A novel methodology for calibrating partial safety factors in the assessment. • Partial safety factors effective in achieving reliability consistent assessment outcomes. • Facilitate performance-based pipeline integrity management practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".