Influence of Strain Hardening Model on the CorLAS™ Model for Cracked Pipelines
Bibliographic record
Abstract
Abstract Underground steel pipelines may experience failure due to the occurrence of cracks or crack-like anomalies as a result of internal and external factors such as manufacturing imperfection and geotechnical movement. Metallic materials like steel often undergo strain hardening as deformation increases. The strain hardening characteristics of materials are usually described by strain hardening models. Accurate approximations of the stress-strain curves are essential for numerical simulations. For pipelines containing longitudinally-oriented cracks, a software-based model often referred to as CorLAS™ is widely accepted and commonly used by the pipeline industry to estimate the failure pressures. In CorLAS™, the stress-strain behavior of pipeline steel is modeled based on a simple power-law relationship known as the Hollomon equation. However, the Hollomon model cannot characterize the full-range strain hardening behavior of metallic materials, which is an approximation by design. Additionally, the strain-hardening exponent, n, in the CorLAS™ model is estimated based on an expression using yield strength and ultimate tensile strength. By contrast, the n value in mathematical models such as the Ramberg-Osgood equation, Swift equation, Ludwik equation, Ludwigson equation can be evaluated by using curve-fitting regression techniques, i.e., fitting the experimental true stress versus true strain data to the empirical models. This paper reviews the most frequently used strain hardening formulas and explores the applicability and accuracy of these stress-strain models including the hardening exponent expression in CorLAS™ (Version 2). This is followed by a sensitivity study to investigate the effect of n on the failure pressure predicted by CorLAS™. The holistic accuracy of CorLAS in predicting burst pressure, compared to other widely accepted models, is not explored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".