Identifying Potential Pipe Failures: Toronto Case Study
Notice bibliographique
Résumé
The issue of aging water infrastructure is a widely acknowledged concern in Canada.Moreover, with C$72 billion tied up in water and wastewater infrastructure in Ontario alone, it is a significant problem that is intensifying with time.In this context, pipebreak failures are a key source of contaminant incursion into the drinking water supply system.Water, once it has entered the distribution system, is beyond the last point of treatment (with the exception of the incidence of chlorine booster stations), so if there is a loss of chlorine residual, any biological contaminant(s) may cause health impacts for the consumer.Those responsible for providing water to consumers have significant concerns with any failures in the water distribution network.Since portions of Canada's water distribution networks may be reaching the end of their useful life, the need exists for a reliable method to identify the pipes most susceptible to failure, as part of proactive decision-making leading to planning for repair, replacement, and rehabilitation for municipalities (e.g.Cullinane et al, 1987;Lei and Saegrov, 1998;Male et al, 1990;Shamsi, 2006).In this context, the chapter describes a model formulation based on assessment of statistics of pipe breakage to provide dimensions of the decision-making procedure in terms of probabilities of future pipe breakage.Application of the model is demonstrated herein through analyses of the pipe break database from the Greater Toronto Area Identifying Potential Pipe Failures: Toronto Case Study (GTA).While general in structure, the model is developed specifically from incidents of failure, as recorded, to then provide the probabilities of future breakage over alternative timeframes.Underground pipes have carried drinking water in the GTA for human needs for more than a century.The distribution network components are primarily out of sight, but failures do occur.For example, approximately 32% and 36% of pipes in the Scarborough and Etobicoke databases, respectively, have broken at least once.Dimensions of failure from pipe/valve/connection include loss of water from the distribution systems, and may include ingress of contaminated water into the distribution systems.For example, 13% of municipal piped water is lost in distribution system leaks and this value is as high as 30% in some communities (Environment Canada, 2004).The consequences of failure may be extremely serious and include contamination of drinking water (e.g.LeChevallier et al., 2003), creation of traffic hazards, and business and social disruption, and imposition of significant repair costs.Therefore, it is highly desirable for water infrastructure engineers and managers to have numerous capabilities to monitor, to assess the condition of, and to predict the failure potential for, water distribution pipes.Gaining an understanding of the condition of the water distribution system, and the potential for failure of elements of the system, is complicated by the wide array of underground water pipes in use today.Pipe materials used include concrete, asbestos cement, cast iron, ductile iron, steel and PVC, each with their own properties and characteristics that influence longevity of service of the individual pipes.Additionally, two pipes of the same material will perform differently according to such features including their respective diameters, quality of water flowing through them, soil type, operational conditions, traffic patterns, and installation and construction practices.Improved understanding of the issues associated with pipe breakage requires knowledge and data related to the processes that lead to pipe corrosion and deterioration, climatic effects on pipe networks, and the development of scientific and innovative approaches for monitoring and maintenance, such as cathodic protection of pipes and in-situ lining for repair.Factors affecting pipe integrity include pipe material, soil characteristics, climatic conditions, operational pressures, age, diameter, and construction and maintenance practices.With improved characterization of the role of these processes in quantifying pipe breakage potential, researchers and water infrastructure engineers can develop improved multidimensional techniques to aid informed decision-making processes from a
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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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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.
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 ».