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Record W172912957

Impact du remplacement des conduites d'aqueduc sur le nombre annuel de bris.

2000· article· fr· W172912957 on OpenAlexaboutno aff
Geneviève Pelletier

Bibliographic record

VenueEspaceINRS Institutional Digital Repository (Institut National de la Recherche Scientifique) · 2000
Typearticle
Languagefr
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

L'état des infrastructures municipales est inquiétant et semble se détériorer rapidement. Des outils doivent être développés pour évaluer l'état structural présent et futur de ces Infrastructures Une Stratégie de modélisation, inspirée de l’analyse de survie et utilisant le nombre annuel de bris répertoriés (historique de réparations) sur les conduites d'aqueduc comme indicateur de l'état structural d'un réseau d'aqueduc, a été développée pour des municipalités possédant de courts historiques de bris. La prédiction des bris d'aqueduc dépend fortement du nombre de bris déjà recensés sur les conduites. En effet, le fait qu'une conduite ait déjà brisé est un bon indicateur du fait qu'elle brisera de nouveau. Afin de tenir compte de cette observation, les données relatives aux bris de conduites d'aqueduc ont été séparées en deux strates qui ont été modélisées séparément : les premiers bris et les bris subséquents. Le modèle simple à trois paramètres développé requiert un minimum de données, généralement faciles à obtenir même dans des municipalités disposant de peu de données. Les problèmes rencontrés lors de la structuration des données des quatre municipalités québécoises ont enrichi notre réflexion quant à la façon dont devraient être archivées les données sur les conduites et les bris, afin de permettre l’identification des facteurs qui ont une influence sur le taux de bris, et ainsi éclairer les gestionnaires dans leurs décisions sur les interventions. La réflexion entourant le choix d'une stratégie de modélisation et le développement d'une stratégie de calage adaptée aux municipalités possédant de courts historiques de bris sont les principaux aspects originaux de la thèse. Municipal water infrastructures seem to be in poor condition and deteriorating rapidly. Tools are \nneeded to assess the present and future structural states of these infrastructures. A modelling \nstrategy, inspired by survival analysis and using the annual number of recorded water pipe breaks \n(from repair records) as an indicator of the structural state of a network, was developed for \nmunicipalities with brief recorded pipe break histories. Pipe breakage behavior depends strongly on \nthe number of previous breaks experienced by a pipe. To take this into account, pipe break data was \ndivided into two strata (first breaks and subsequent breaks), modeled separately. The resulting \nsimple three-parameter pipe break model requires minimal and readily available data. \nProblems encountered while structuring the four Quebec municipalities' data have deepen our \nunderstanding of how data on pipes and breaks should he archived, in order to facilitate the \nidentification of factors that have an impact on break rates. These risk factors can then be used to \nhelp managers in their decision making conceming interventions on their network. The choice of a \nmodelling strategy and the development of a calibration strategy adapted to municipalities with brief \nrecorded pipe break histories are the main original aspects of this thesis.

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)0.002
metaresearch head score (Gemma)0.011
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.059
GPT teacher head0.284
Teacher spread0.225 · 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

Citations8
Published2000
Admission routes1
Has abstractyes

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