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Record W2606456276 · doi:10.14796/jwmm.r227-22

Identifying Potential Pipe Failures: Toronto Case Study

2007· article· en· W2606456276 on OpenAlexafffundvenueabout
Corinne J. Schuster‐Wallace, Edward A. McBean, Khizar Hayat

Bibliographic record

VenueJournal of Water Management Modeling · 2007
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsForensic engineeringEngineering

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.236
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2007
Admission routes4
Has abstractyes

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