A Risk Approach for Distribution Pipeline Systems
Notice bibliographique
Résumé
Unlike the circumstance associated with transmission pipelines, where variables that are attributes of risk are typically widely available in GIS systems or in other databases that are geo-referenced to linear assets, risk data for distribution systems are not typically linearly referenced to what is essentially a network system. Therefore the manner in which risk is calculated and displayed for distribution systems must differ significantly from the way these functions are performed on transmission pipelines. In distribution systems, failure (defined as the loss of containment) and the contributors to the likelihood of failure, is often highly correlated to system-specific circumstances, such as type of material used, installation era, and operating environment. These correlations between cause-and-effect as they relate to failure likelihood in distribution systems are not widely recognized on a universal basis, such as they might be in transmission pipeline environments, but are typically unique to each operating system. Because system data for distribution networks is not typically available in a manner that can be linearly geo-referenced to pipeline coordinates the way it is for transmission systems, the convention of mapping risk to pipeline dynamic segments as a function of risk attributes that exist within those dynamic segments is not achievable for distribution systems the way that it is for transmission systems. Therefore, the most effective strategy for performing risk assessments in distribution systems is to create a database in which existing incident data can be correlated to system attributes, and then to use those correlations to create cause-and-effect relationships between system attributes and failure likelihood. Consequences are characterized in terms of the operating environment (e.g., wall-to-wall, residential, etc.), leak magnitude, type of facility (mains vs. service lines), and special mitigating or exacerbating factors, such as availability of excess flow valves, or the presence of inside meters. A risk assessment methodology has been developed that accommodates the above constraints and that meets the stated objectives, and which is well-suited to the distribution system data infrastructure that is typical of most operators. Because the risk assessment approach leverages existing databases and incident reporting structures, it lends itself to automation, and re-evaluation on a regular basis. Reporting is facilitated by a ‘heat map’, which provides immediate insight as to the drivers of risk for each system sub-group having similar design, materials, and operating characteristics.
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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,006 | 0,009 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,004 |
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 ».