Fatigue Life Assessment for NPS30 Steel Pipe
Bibliographic record
Abstract
Pipeline is the common mode for transporting oil, gas, and various petroleum products. Aging and corrosive environment may lead to formation of various defects such as crack, dent, gouge, and corrosion. The performance evaluation of field pipelines with crack defect is important. Accurate assessment of crack depth and remaining fatigue life of pipelines with crack defect is vital for pipeline’s structural integrity, inspection interval, management, and maintenance. An experimental based research work was completed at the University of Windsor for developing a semi-empirical model for estimating the remaining fatigue life of oil and gas pipes when a longitudinal crack defect has formed. A statistical approach in conjunction with fracture mechanics was used to develop this model. Statistical analysis was undertaken on CT specimen data to develop this fatigue life assessment model. Finite element method was used for determining the stress intensity factor. The fatigue life assessment model was then validated using full-scale fatigue test data obtained from 762 mm (30 inch) diameter X65 pipe. This paper discusses the test specimens and test data obtained from this study. Development and validation of the fatigue life assessment model is also presented in this paper.
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 | 0.000 | 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) | 0.002 | 0.001 |
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".