A Statistical Model for the Prediction of SCC Formation Along a Pipeline
Notice bibliographique
Résumé
Near-neutral stress corrosion cracking (SCC) is an operational integrity problem experienced by pipeline transporation companies since the 1970’s. Current in-line inspection (ILI) technology allows for the detection of SCC in pipelines using ultrasonic measurement. However, these tools have size limitations (not available for small diameter pipelines) and can only accurately detect cracks above a certain threshold dimension. Predictive modeling of SCC has been used when direct detection was not feasible. To date, predictive models have focused mainly on establishing quantitative relationships between environmental factors and SCC formation or growth. In general, models used to predict SCC growth have been more successful than models used to predict the location of SCC formation. A model to predict locations of SCC formation has been developed, in conjunction with a pipeline operator, by statistically analyzing data related to locations where SCC was either found or not found during investigative digs on a particular pipeline. Data acquired at the investigative dig sites (such as soil conditions, drainage patterns and local geography) was incorporated into the analysis. In addition, data acquired for the entire length of the pipeline (such as geometry, metal loss features, close-interval cathodic protection readings and operating pressures) was combined with the dig site data in the analysis process. The combined data set was analyzed using statistical regression techniques and various multi-variable logistic regression models were created. Misclassification analysis and regression tress were used to determine the most accurate model for application to the pipeline. The model was then applied to the pipeline to determine probabilities of SCC at specified increments along its length (approximately every 20 metres). Ten locations with high SCC probabilities were selected for verification excavation. In addition, one site with a lower SCC probability was chosen for excavation. Of the ten high-probability locations, SCC was discovered at seven sites. At the lower probability site, SCC was not discovered. The combined success rate of themodel was 73%, a significant improvement over predictive models previously applied to the pipeline. Additional investigative digs are planned to further test the model and to compare its predictions to SCC detected by a recently developed ultrasonic ILI tool. By examining the occurrence of SCC using statistical methods, the ability to make an unbiased prediction of the probability of SCC along a pipeline of interest has been achieved. The pipeline operator has gained an increased ability to assess the likelihood of SCC along its pipeline, showing due diligence in mitigating the risks associated with this pipeline integrity concern.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».