Remaining Collapse Capacity of Corroded Pipelines
Bibliographic record
Abstract
It is known that, for given pipe material and diameter, collapse capacity of a plain pipe subjected to external pressure is proportional to the second or third power of wall thickness. In lieu of sophisticated numerical models and experimental data, conservative approaches such as those in which thickness losses at corrosion defects are extended to the entire circumference have been adopted in practices to assess the collapse resistance of corroded pipes. This reduced wall thickness is then used in the design equation of plain pipe to predict remaining collapse capacity. Such conservative assumptions result in substantial reduction of collapse capacity for pipelines with localized corrosion defects. During the course of a multiple-year PRCI research project, results of full-scale collapse tests and three-dimensional finite element analysis demonstrated that the reduction of collapse capacity was less than 10% for defects with a depth of 50% wall thickness, an axial length of one diameter and a circumferential width of half a diameter. These findings illustrated that the actual collapse capacity of corroded pipes is significantly higher than that estimated according to the conservative assumptions. This paper presents the development of a reliability-based, practical assessment method that allows remaining collapse capacity of corroded pipelines be determined based on defect size data obtained from in-line inspections. Work involved included characterization of corrosion defects, full-scale collapse tests, validation of finite element models using experimental data, analysis of parametric cases using finite element models, development of empirical equation based on experimental and numerical results, and calibration of partial safety factors which addressed the uncertainties associated with model error, load variation, and sizing inaccuracy of corrosion defects. Practical implications of the proposed assessment method were evaluated based on selected examples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".