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Enregistrement W2900114947 · doi:10.1115/ipc2018-78740

Flood Monitoring: Evaluating Action Response Time Relative to Warning Time

2018· article· en· W2900114947 sur OpenAlexaff
S. L. Davidson, Gerald R. Ferris, Joel Van Hove, Joel Babcock, Jan Bracic

Notice bibliographique

RevueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2018
Typearticle
Langueen
DomaineEngineering
ThématiqueWater Systems and Optimization
Établissements canadiensBGC Engineering (Canada)
Organismes subventionnairesnon disponible
Mots-clésFlood mythWarning systemRisk analysis (engineering)Pipeline (software)Computer scienceFlooding (psychology)Pipeline transportEnvironmental scienceReliability engineeringEngineeringBusinessEnvironmental engineeringGeography

Résumé

récupéré en direct d'OpenAlex

Flood monitoring is one method currently being used by the pipeline industry to provide alerts when flooding is approaching, or has exceeded, levels that could create hydrotechnical conditions that threaten pipeline integrity. Flood monitoring does not provide protection from hydrotechnical hazards or reduce the probability of failure, but can lower risk by providing advanced warning, allowing operators to initiate actions that reduce the consequences of failure in the rare event that pipeline integrity is threatened by hydrotechnical forces. Pipeline pressure reduction, shut-in, purge, and spill response mobilization are all examples of actions commonly used to reduce failure consequence. However, these actions require time to execute, ranging from a number of hours to a number of days, depending on factors such as site location, valve spacing, and product type. The effectiveness of flood monitoring as a consequence reduction strategy is contingent on having sufficient time to implement the flood response action. In designing a flood monitoring program, it is necessary to ask: can flood monitoring provide sufficient advanced warning for an action plan to be fully executed before pipeline integrity is compromised? The present study evaluated 35 high priority pipeline watercourse crossings, to estimate the flood return periods at which actions could be taken that correspond to warning times of 12, 24, 48, and 72 hours before the critical flood (i.e., a conservative estimate of the flow at which fatigue failure is considered possible) and to evaluate the feasibility of flood monitoring as a short-term risk management strategy prior to mitigation. The 35 crossings are currently scheduled for mitigation and rely on flood monitoring as an interim risk management tool. The rate of increase in flood discharge during all previously recorded flood events at each real-time monitoring gauge was first obtained to estimate the rate of flow increase during the critical flood event. Of the 35 crossings, 33 had a maximum warning time of less than 48 hours. Using a 24-hour warning time, 10 of the 35 crossings have a warning flow of less than a 1 in 5-year flood. The results show that the ‘action initiation flood level’ for more than 90% of the most susceptible watercourse crossings may be too low to be practical; at crossings where more than 48 hours of response time is required, flood monitoring may not significantly reduce hazard consequence as the action response plan may not be fully executed prior to pipeline failure. Pipeline failures are rare, and flood monitoring provides a useful monitoring approach for short-term management in many watercourses. However, these results demonstrate the importance of evaluating the required action response time relative to the available warning time for each watercourse crossing to confirm that flood monitoring will achieve the risk reduction expected by the operator. If flood monitoring is determined to be impractical because the action initiation flood is too low, it may provide justification for initiating other management actions (e.g., flood forecasting, purging prior to the flood season, or elevating such sites on the priority list for physical repairs).

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,351
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,022
Tête enseignante GPT0,268
Écart entre enseignants0,246 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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