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Record W3139773084 · doi:10.2166/wpt.2021.026

Current water main rehabilitation practice using trenchless technology

2021· article· en· W3139773084 on OpenAlexaff
Yichen Wu, Chao Kang, Mohammad Molavi Nojumi, Alireza Bayat, George Bontus

Bibliographic record

VenueWater Practice & Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Northern British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsTrenchless technologyRehabilitationMains electricityCurrent (fluid)EngineeringConstruction engineeringCivil engineeringEnvironmental sciencePipeline transportEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract The market for water infrastructure rehabilitation is growing rapidly due to the increasing age of underground utilities. Currently, two common water main rehabilitation methods exist: cured-in-place pipe (CIPP) and polymer spray-on coatings. CIPP can provide structural support for both internal and external loads, while spray-on techniques provide chemical resistance as well as adding minor strength to the existing pipe. This paper summarizes water main rehabilitation practice using CIPP and spray-on methods. The history of trenchless rehabilitation technology is discussed, as well as current methodologies and products for water mains. The design, installation, and monitoring of water main rehabilitation products are also summarized, along with the associated risks. Quality assurance and control (QA/QC) methods are included for evaluating existing products and procedures.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.252
Teacher spread0.245 · 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 designNot applicable
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

Citations16
Published2021
Admission routes1
Has abstractyes

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