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Record W2109323826 · doi:10.1115/ipc2006-10464

Development of a Pipeline Surface Loading Screening Process

2006· article· en· W2109323826 on OpenAlexaffabout
David Warman, Jules Chorney, James D. Hart

Bibliographic record

VenueVolume 1: Project Management; Design and Construction; Environmental Issues; GIS/Database Development; Innovative Projects and Emerging Issues; Operations and Maintenance; Pipelining in Northern Environments; Standards and Regulations · 2006
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsPetroleum Technology Alliance Canada
Fundersnot available
KeywordsPipeline (software)Pipeline transportProcess (computing)Computer scienceReliability engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Pipeline Operators receive numerous requests annually to cross their pipelines. In many of these cases detailed analysis using a number of different methods are performed since no simplified approach is available. The Canadian Energy Pipeline Association (CEPA) with Kiefner and Associates, Inc. undertook the development of a screening methodology for vehicle loading. The hope is a standard approach to these analyses might be established to assist pipeline operating companies. This paper describes an approach detailing the development and implementation of a simplified screening process to assess the effects of surface loads on buried pipelines. A design basis was established based on a literature review to identify theoretical models, standards, codes, and recommended practices that are currently used to assess the surface loading effects on buried pipelines. This design basis was incorporated into a methodology utilized to develop a screening tool which provides a simple “pass/no pass” determination and is based on attributes which are generally easy to obtain (e.g., wheel or axle load, ground surface loading pressure, depth of cover, maximum allowable operating pressure and design factor). Situations which pass the initial screening would require no additional analysis while situations that do not pass the initial screening may need to be evaluated on a more detailed basis. Simplified graphs have been developed to assist in additional screening prior to performing a more detailed evaluation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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.

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

Citations7
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueVolume 1: Project Management; Design and Construction; Environmental Issues; GIS/Database Development; Innovative Projects and Emerging Issues; Operations and Maintenance; Pipelining in Northern Environments; Standards and RegulationsSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207