An Approach for Prioritizing Pipeline Water Crossings (WC) for Effective Mitigation and Monitoring
Notice bibliographique
Résumé
This paper provides an approach for assessing and classifying riverine pipeline crossings to prioritize effective mitigation and monitoring. These processes require understanding of and accounting for channel processes, river dynamics, geomorphic principals and soil mechanics to estimate bed scour and bank erosion degradation mechanisms at water crossings and their potential effects on the pipeline. The intent of this paper is to share generic experiences in ranking water crossings based on their susceptibility to and identification of integrity threats under multiple existing and future hydrologic scenarios causing potential for pipeline exposure, spanning or damage. The intent is not to present or provide an analysis or review of the various methods for estimating channel bed or bank erosion. The details of such calculations are highly site specific and a variety of both qualitative and quantitative methods can be applied depending upon available site data, and as such, are outside the scope of the work presented here. Pipelines are static features within a dynamic environment with rivers and floodplains representing some of the most active areas within a landscape. Rivers can change course, migrate, deepen, and widen slowly over time or suddenly during large flood events. These hydrologic effects can impact existing pipelines thereby putting pipelines at risk for damage or failure. Understanding how rivers alter the landscape and transport water and sediment from the mountains to the sea provides a framework for realizing the potential toll that riverine changes can have on pipeline infrastructure. Further, integrating analysis of how rivers at specific pipeline crossing locations are likely to change can increase the effectiveness in protecting the environment during the design, construction, operation and integrity management of pipelines at river crossings. The paper provides an approach comprised of five (5) stages: 1. WC Inventory and Desktop Data Gathering 2. Screening Process: Preliminary WC Classification 3. Detailed Assessment 4. Final WC Classification, Prioritization, and Risk Assessment 5. Development of Mitigation and Monitoring Strategies This paper also presents two (2) case studies illustrating how assessing the geomorphic condition and processes of the river system being crossed by pipelines provides for a better understanding of susceptibility to existing hydro-geotechnical threats to the pipeline as well as the susceptibility for flood-related forces in the future. The first case study illustrates how changes to a river’s cross section as a result of construction activities upstream of a pipeline water crossing can cause significant and potentially damaging impacts, downstream. The second case study reinforces the importance of understanding the history of watershed and channel changes over time, both at the specific water crossing location, but also both upstream and downstream from the crossing itself to be able to identify and understand all potential threats to pipelines located within rivers and floodplains. A method for assessing and classifying the magnitude and probability of flood related risk at each case study is discussed. These cases are presented as generic examples for educational purposes only as every pipeline has its own specific characteristics conditions with jurisdiction-specific regulatory requirements requiring process customization and enhancements.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,009 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».