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.
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.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".