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Record W2462005271 · doi:10.1061/9780784479957.043

Managing the Risk of Critical Water Trunk Mains: A Municipal Perspective

2016· article· en· W2462005271 on OpenAlexaff
Yafei Hu, Wenqing Yang, Chris Macey, Michael Moncrieff, Amir Agha

Bibliographic record

VenuePipelines 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsManitoba Beekeepers' AssociationSaskatchewan Health AuthorityUniversity of Regina
Fundersnot available
KeywordsMains electricityRisk assessmentAsset managementFailure mode, effects, and criticality analysisReliability engineeringEngineeringFailure mode and effects analysisComputer scienceRisk analysis (engineering)Business

Abstract

fetched live from OpenAlex

In early 2012, the City of Regina started a risk assessment program for its critical trunk mains in its water supply/distribution system. The program included the rating of initial condition and failure consequence ratings of the mains, and the development of a risk model for the system. It also recommended an annual condition assessment program, which combines desktop analysis and field inspections to maximize knowledge of the trunk’s condition in the most effective manner. As an initial and simplified step, pipe condition in the risk model was estimated based on pipe age, size and material. However, the foundation of a condition assessment program should be built on the applied loadings on the trunks versus the residual resistance strength of the trunks as determined in the condition assessment program, as this is the true measure of failure probability. Considering the high cost of a condition assessment program, an initial assessment project was conducted to rationalize both the economics and degree of certainty associated with different inspection techniques in order to develop an optimum program for the long term. This paper presents the framework developed for managing the risk of the City’s trunk mains, which includes an asset criticality model with trunks ranked based on their relative consequence of failure, a condition assessment strategy that maximizes the use of existing failure records and in-direct assessment data, and a set of decision criteria that can be used to identify and prioritize inspection, maintenance, and capital requirements for a comprehensive mitigation plan for the trunks. Also presented in the paper were the results of the initial assessment project. Both traditional and advanced techniques were deployed to inspect the condition of two trunk sections. The inspection indicated severe internal pits and potential external coating deficiencies/pipe corrosion. Field excavation was performed to verify the inspection results.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0060.003
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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