Machine Learning Tools to Predict the Burst Capacity of Pipelines Containing Dent-Gouges
Bibliographic record
Abstract
Abstract Dent-gouges as a result of the mechanical damage have serious implications for the burst capacity of oil and gas pipelines. The burst capacity of pipelines containing dent-gouges is lower than that of the same plain dented pipelines without gouges and that of the same gouged pipelines without dents. The well-known burst capacity prediction model adopted by the European Pipeline Research Group, i.e. the EPRG model, results in predictions of the burst capacity with high variability. In this study, a machine learning tool is employed to improve the predictive accuracy of the EPRG model for pipelines containing dent-gouges. To this end, a relatively large number of full-scale burst tests of pipe specimens containing dent-gouges are collected from the literature. The Gaussian process regression (GPR) technique, which is a class of non-parametric Bayesian model widely used in the machine learning, is employed to improve the EPRG model based on the collected full-scale burst test data. The full-scale burst tests are used to evaluate the hyper-parameters involved in the GPR analysis and validate the predictive accuracy of the improved EPRG model after the application of GPR. To facilitate the practical application of the improved EPRG model, a computer program with a graphic user interface (GUI) is further developed to compute the burst capacity of pipelines containing dent-gouges by inputting key parameters such as the pipe geometry and material properties as well as sizes of the dent and gouge through a GUI. This research will improve the fitness-for-service assessment of pipelines containing dent-gouges.
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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 | 0.002 | 0.001 |
| 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".