Identification of the Cause of Variability of Probability of Failure for Burst Models Recommended by Codes/Standards
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.
Bibliographic record
Abstract
Failure probability of oil and gas pipelines due to external corrosion defects can be estimated using corrosion growth model and the evaluation of remaining strength. Codes/standards have been developed for the assessment of the remaining strength of corroded pipeline. The remaining strength and the operating pressure were considered to develop the limit state equation and consequently the failure probability of the burst models recommended by codes/standards. In the present paper, comparative analyses of the failure probability estimated by the codes/standards were conducted, using Monte Carlo simulation and first order second moment methods. The analysis revealed that the failure probability of the burst models recommended by codes/standards varies significantly for the same defects size. The study further explored the cause of variability in failure probabilities. The study observed that different defect shape specifications (rectangular, parabolic, etc.) and different stress concentration factor derivations (different contributions of l) for burst pressure estimation are responsible for high variability in the probability of failure. It is important to reduce variability to ensure unified risk-based design approach considering any codes/standards.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it