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Record W2291790540 · doi:10.14288/1.0076464

Reliability analysis of water distribution networks using minimum cut set approach

2015· article· en· W2291790540 on OpenAlexaffabout
Azhar Uddin Mohammed, Tarek Zayed, Osama Moselhi, Alaa Alhawari

Bibliographic record

VenuecIRcle (University of British Columbia) · 2015
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsReliability (semiconductor)Reliability engineeringSet (abstract data type)Component (thermodynamics)ComputationEngineeringComputer sciencePower (physics)

Abstract

fetched live from OpenAlex

Canadian Water and Wastewater Association (CWWA) estimated the cost to replace 112,000 km of water mains in Canada to be 34 billion Canadian Dollars. Reliability analysis of water distribution networks (WDNs) is an important aspect in planning and operation of a WDN and hence plays an important role in the efficient use of allocated budget. In general, reliability analysis is classified into mechanical reliability and hydraulic reliability. Mechanical reliability is defined as the ability to function even when some components are out of service or there is any mechanical break. Hydraulic reliability is concerned with delivery of the specified quantity of water to a specific location at the required time under the desired pressure. This paper introduces a methodology for evaluating mechanical reliability of WDNs using the minimum cut set approach. The methodology involves the computation of mechanical reliability at the component (pipe, hydrant etc.), segment (collection of pipes and components) and network levels. An illustrative example is worked out to demonstrate the use of the developed methodology.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.165
Teacher spread0.153 · 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 designSimulation or modeling
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

Citations0
Published2015
Admission routes2
Has abstractyes

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