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Enregistrement W1998163104 · doi:10.1115/ipc2014-33474

Effective Consequence Evaluation for System Wide Risk Assessment of Natural Gas Pipelines

2014· article· en· W1998163104 sur OpenAlexaff
Aleksandar Tomić, Shahani Kariyawasam, Pauline Kwong

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueRisk and Safety Analysis
Établissements canadiensTransCanada (Canada)
Organismes subventionnairesnon disponible
Mots-clésRisk assessmentRisk analysis (engineering)Risk managementPipeline (software)PopulationComputer sciencePipeline transportRanking (information retrieval)Probabilistic logicReliability engineeringEngineeringArtificial intelligenceBusiness

Résumé

récupéré en direct d'OpenAlex

System Wide Risk Assessment (SWRA) is an integral part of an Integrity Management Program (IMP), and it is the first step in most IMPs. Risk is the expected value of loss (often expressed as damage per year, i.e. expected number of annual injuries or fatalities). Risk is calculated as the product of the Probability/Likelihood of Failure (LoF) and the consequence of failure, where failure is defined as a loss of containment event. Hence, it is necessary to calculate both the Likelihood and the consequences of failure in order to accurately model risk. For natural gas pipelines, consequence is primarily human safety-based. The primary threat to the population is the effect of the thermal radiation due to ignited pipeline ruptures. Currently, most pipeline industry system wide risk assessment models are qualitative risk models, where consequence is generally characterized by class, relative population measures, or some other relative measure. While this may be adequate for some relative risk ranking purposes, it is generally not accurate in representing the true consequences and the arbitrary nature leads to poor representation of actual consequences. Qualitative risk models are also highly subjective, and can have a high degree of bias. Thus, in this study, quantitative LoF assessment and a rigorous quantitative consequence model were used to make the risk assessment process more accurate, more objective, and transparent. The likelihood algorithm developed in this study is described in a companion paper. It should be noted that a quantitative estimate is never completely objective as subjective assumptions and idealizations are still involved, however it provides a framework to make it as objective as possible. The consequence model implemented in this study is highly quantitative, and it depends on the pipeline properties (i.e. diameter, MAOP etc.) in addition to the structure properties (i.e. precise location and type of structures). The lethality zone utilized in the consequence model is a curve which has 100% lethality at the point of rupture but recedes in lethality as the point of concern moves away from the rupture location. The lethality curve is calculated using the PIPESAFE software [6] that is developed by rigorous analytical, experimental, and verification work. This ensures that the lethality curves are pipeline specific. Furthermore, the position of the structures inside the lethality zones is taken into consideration, which means the structures located closer to the pipeline see a higher degree of lethality than the structures further away from the pipeline. Risk is represented by two specific, well defined measures: Individual Risk (IR), and Societal Risk (SR). These two measures are well accepted concepts of risk that go beyond the pipeline industry, and are particularly used in the pipeline industry in countries where quantitative risk is required by regulation (e.g. UK and Nederlands). IR takes into account the inherent risk of the pipeline to the single individual who may happen to be in the vicinity of the pipeline. SR, on the other hand, takes into account known population centers, settlements, and structures to define the risk to communities. When risk is calculated quantitatively, it is possible to use well defined and widely accepted criteria to determine the acceptability of risk in terms of IR and SR criteria for all pipelines. The advantages of using IR and SR are discussed and shown through implemented examples.

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)0,014
score de la tête « metaresearch » (Gemma)0,012
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,890
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0140,012
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
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 Opus0,049
Tête enseignante GPT0,429
Écart entre enseignants0,380 · 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'étudeSimulation ou modélisation
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
Publié2014
Routes d'admission1
Résumé présentoui

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