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Record W3109150949 · doi:10.1139/cjce-2020-0287

Managing sewerage networks using both failure modes, effects and criticality analysis (FMECA) and analytic hierarchy process (AHP) methods

2020· article· en· W3109150949 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsFailure mode, effects, and criticality analysisSewerageAnalytic hierarchy processReliability engineeringFailure mode and effects analysisProcess (computing)Consistency (knowledge bases)CriticalityEngineeringComputer scienceRisk analysis (engineering)Operations researchEnvironmental engineeringBusiness

Abstract

fetched live from OpenAlex

This paper proposes a methodology for managing complex sewerage networks based on the concomitant use of two performance evaluation methods, namely, the failure modes, effects, and criticality analysis (FMECA) and the analytical hierarchy process (AHP). The FMECA is used to determine the risks of structural failures making it possible to establish a methodology for managing these failures. The AHP is used to check the relationship consistency between the performance indicators allowing the determination of the overall performance (OP). This proposed methodology was utilized for the urban sewerage network of Oued-Kniss in the city of Algiers, Algeria, as part of the efforts engaged in for sustainable and efficient management.

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.

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: none
Teacher disagreement score0.959
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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