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Record W2899718081 · doi:10.1115/ipc2018-78736

The Optimum Pipeline Burial Depth Considering Slow Downslope Soil Movement and Seasonal Temperature Variation

2018· article· en· W2899718081 on OpenAlexaffabout
Mohammad Katebi, Hongwei Liu, Pooneh Maghoul, James Blatz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPipeline (software)Geotechnical engineeringGeologyStress (linguistics)ThermalEnvironmental scienceDisplacement (psychology)Pipeline transportHeat transferMeteorologyEngineeringMechanicsGeographyMechanical engineering

Abstract

fetched live from OpenAlex

Thermal stress induced in a buried pipeline due to temperature variation is of great concern in Canada due to its extreme cold winter and warm summer. Thermal stress decreases by increasing the pipe’s burial depth while the interaction forces due to ground displacement increase by increasing the burial depth. As a result, the optimum burial depth of a pipeline is of great importance to pipeline companies to minimize interactions between the pipeline and soil in case of temperature variations and ground displacements. Thermal stress is estimated from a heat transfer analysis considering the phase change in the soil using COMSOL. Soil-pipeline interaction based on 1984 ASCE Guidelines [1] is used for considering the effects of ground movements. The combined stress on the pipeline is estimated as a function of burial depth and is presented in a curve for design purposes. Numerical analysis by ABAQUS shows the adequacy of the presented curve.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.000

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 head0.023
GPT teacher head0.231
Teacher spread0.208 · 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 designSimulation or modeling
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

Citations1
Published2018
Admission routes2
Has abstractyes

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