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Record W2999739946 · doi:10.1061/9780784482599.009

A Study on the Performance of Insulation for Buried Utilities in Cold Regions

2019· article· en· W2999739946 on OpenAlexafffund
H. Liu, Pooneh Maghoul, Ahmed Shalaby, Leonnie Kavanagh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEnergy and Environmental Systems
Canadian institutionsUniversity of Manitoba
FundersCMC Microsystems
KeywordsFrost (temperature)Service lifeEnvironmental scienceGeotechnical engineeringForensic engineeringEngineeringMaterials scienceMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

Damage due to the frost penetration in soils can be detrimental to buried utilities such as water and gas pipes. It would not only reduce their service life but may also induce service interruptions and public safety concerns. Therefore, buried pipes should be located below the frost depth to prevent them from the frost damage. An alternative approach to protect buried utilities against frost damage and reduce the construction cost is to install insulations over and around the pipes. This paper aims to study the performance of the insulation materials with various geometries to prevent buried utilities from the frost damage in frost-susceptible soils. For this purpose, a comparative study is performed to determine the effect of phase change on the performance of the insulating foam with various geometries. Also, various geometries of insulation including horizontal, inverted U-shaped, and cylindrical insulations are modeled in order to present the best insulation configuration.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.047
GPT teacher head0.283
Teacher spread0.235 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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