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Record W2326944796 · doi:10.1061/40994(321)51

An Integrated Management Approach for Critical Water Mains

2008· article· en· W2326944796 on OpenAlexaff
Hesham Osman, Kevin Bainbridge, Marshall Gibbons, Chris Macey, Ross Homeniuk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsMains electricityAsset managementFailure mode, effects, and criticality analysisComputer scienceRationalization (economics)Risk analysis (engineering)Reliability engineeringCriticalityOperations researchEngineeringBusinessFailure mode and effects analysis

Abstract

fetched live from OpenAlex

The concept of `criticality' plays a significant role in determining the management approach for water mains. Critical water mains are defined as those whose failure would have substantial economic, environmental or social impacts on the communities they serve. Whereas `run-to-failure' approaches are tolerated with non-critical water mains, critical water mains have almost no tolerance for failure. A sound management approach for critical water mains should be built on reliable condition state information and a proactive strategy for collection of this information. The variety of water main condition assessment techniques available to the asset manager coupled with their relatively high cost of deployment require a rational framework for technique selection. In addition, there is a need to rationalize and consolidate the condition rating information revealed by various techniques. This step is essential in order to standardize the decision-making processes (e.g. repair, conduct further tests immediately, schedule next inspection) across the system inventory. As such, the City of Hamilton is currently developing a standard management framework for its critical water main inventory. The framework is composed of two main components: 1) A condition assessment rationalization framework and 2) A condition rating consolidation framework. The assessment rationalization framework is composed of a set of matrices that help the asset manager identify the suitability of each technique (When and Where to use?), the needs associated with each technique (constraints, costs, and impacts) and the expected outcomes of each technique (data reliability). The matrices are especially useful due to the plethora of new assessment techniques being developed as they serve as a standard framework for technique classification and evaluation. The condition rating consolidation framework attempts to standardize the way the results of assessment techniques are interpreted and subsequently used to drive decisions. The framework is developed for Ductile Iron and Cast Iron water mains as they compose the majority of the City's critical inventory. The framework utilizes Fuzzy Logic and the Analytical Hierarchy Process to combine condition rating results into an overall rating for the condition state as well as the expected deterioration rate of the pipe.

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: Methods · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.166

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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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
GenreMethods

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

Citations4
Published2008
Admission routes1
Has abstractyes

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