Reliability-Based Assessment of Cracked Pipelines Using Monte Carlo Simulation Technique With CorLAS™
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Bibliographic record
Abstract
Abstract If not assessed properly, unstable crack growth in pipelines could result in detrimental leaks or ruptures. Fracture mechanics models are typically used to assess the susceptibility of pipelines to fail due to the presence of cracks or crack-like anomalies. To this end, an inelastic (or elastic-plastic) fracture mechanics model, known as CorLAS™ model, has been developed and frequently used by pipeline operators. This paper first reviews the development of the CorLAS™ model and derives the probabilistic characteristics, including mean and coefficient of variation (COV) associated with the CorLAS™ model using a collection of 94 full-scale burst test data from the literature. A comprehensive reliability assessment of cracked pipes based on the CorLAS™ model is performed through the Monte Carlo Simulation (MCS) method. For each reported scenario, the probability of failure (PoF) is calculated by MCS that considers the uncertainties associated with various parameters such as pipe geometry, material properties, and the uncertainty due to the fracture model itself, namely, the model error. Finally, a sensitivity study is conducted considering various input parameters, including pipe grade, pipe diameter, wall thickness, ratio of crack length to depth, ratio of crack depth to wall thickness, and model error COV. The results suggest that the PoFs are highly sensitive to the COV, i.e., the PoFs increase significantly with the increase of the COVs, while the effects of other input parameters on the PoFs are insignificant. It is also shown that the model error COV of CorLAS™ with a value of 13% could serve as a reference value for future model error studies.
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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 | 0.001 | 0.002 |
| 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 it