Classification of failure modes of pipelines containing longitudinal surface cracks using mechanics-based and machine learning models
Bibliographic record
Abstract
Abstract This paper applies the mechanics-based approach and five machine learning algorithms to classify the failure mode (leak or rupture) of steel oil and gas pipelines containing longitudinally oriented surface cracks. The mechanics-based approach compares the nominal hoop stress remote from the surface crack at failure and the remote nominal hoop stress to cause unstable longitudinal propagation of the through-wall crack to predict the failure mode. The employed machine learning algorithms consist of three single learning algorithms, namely naïve Bayes, support vector machine and decision tree; and two ensemble learning algorithms, namely random forest and gradient boosting. The classification accuracy of the mechanics-based approach and machine learning algorithms is evaluated based on 250 full-scale burst tests of pipe specimens collected from the open literature. The analysis results reveal that the mechanics-based approach leads to highly biased classifications: many leaks erroneously classified as ruptures. The machine learning algorithms lead to markedly improved accuracy. The random forest and gradient boosting models result in the classification accuracy of over 95% for ruptures and leaks, with the accuracy of the decision tree and support vector machine models somewhat lower. This study demonstrates the value of employing machine learning models to improve the integrity management practice of oil and gas pipelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".