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Effects of Relaxed Minimum Pipe Diameters on Fire Flow, Cost, and Water Quality Indicators in Drinking Water Distribution Networks

2020· article· en· W3028202307 on OpenAlexaff
John Gibson, Bryan Karney, Yiping Guo

Bibliographic record

VenueJournal of Water Resources Planning and Management · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsMcMaster UniversityUniversity of TorontoSt. Stephen's University
Fundersnot available
KeywordsEnvironmental scienceCapital costWork (physics)Flow (mathematics)Water flowFire protectionService lifeEnvironmental engineeringCivil engineeringEngineeringMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

The use of pipes no smaller than 150 mm (6 in.) in diameter is often recommended for fire protection in North America. This work examines the some of the costs and benefits of this restriction by looking at a single pipe in isolation. First, we argue that North American fire flow requirements are quite conservative by international standards, with European requirements approximately 25% of those in North America. It is shown that smoother 100-mm PVC in place of older, rougher 150-mm cast iron can produce 60% of the available fire flow, in principle still exceeding the European requirement. Furthermore, the estimated capital cost is reduced by 30%, and water age by 56%. No differences in energy use were observed, owing to very low demands in normal service. A simple model of biological growth showed some potential for increased biological growth in smaller pipes, however. Smaller pipes likely have more dynamic shear stresses, which can mitigate discoloration. Overall, there may be many benefits if smaller-diameter pipes are permitted in low-density suburban service. Fundamentally, the amount of water needed to fight modern fires in North America is largely unknown, suggesting a need for additional research.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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 teacher head, 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

Citations12
Published2020
Admission routes1
Has abstractyes

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