Development of a Near-Neutral pH Stress Corrosion Cracking Growth Model for Pipelines Using Machine Learning Algorithms
Bibliographic record
Abstract
Abstract Near-neutral pH stress corrosion cracking (NNpHSCC) is one of the leading causes of failure for buried pipelines. Characterizing the NNpHSCC growth rate accurately remains a challenging task for the pipeline industry. In this study, an NNpHSCC growth model for buried pipelines is developed based on experimental data obtained from full-scale tests conducted at the CanmetMATERIALS of Natural Resources Canada of pipe specimens that are in contact with near-neutral pH environment and subjected to cyclic internal pressures. Four machine learning algorithms, namely the random forest (RF), extremely randomized trees (ET), gradient boosting (GB) and extreme gradient boosting (XGB), are employed to estimate the crack growth rates da/dN from input variables characterizing the pipe geometry, internal pressure and environmental condition. The machine learning models are trained through hyperparameter tuning and k-fold cross validation to improve the model robustness. Model performances are validated and compared using an independent test dataset. This study provides an initial step in using machine learning tools to develop robust NNpHSCC growth models suitable for practical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".