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Record W2127424943 · doi:10.1139/t04-116

Reliability measures for buried flexible pipes

2005· article· en· W2127424943 on OpenAlexvenueno aff
G. L. Sivakumar Babu, R. Seshagiri Rao

Bibliographic record

VenueCanadian Geotechnical Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsDeflection (physics)Geotechnical engineeringReliability (semiconductor)Structural engineeringSafety factorModulusEngineeringReliability engineeringFactor of safetyMaterials science

Abstract

fetched live from OpenAlex

The safety of infrastructure facilities such as buried pipelines is the primary objective of engineering design. An improved measure of safety and reliability of these structures can be obtained with concepts of probability. The assessment of safety involves uncertainties at various stages, such as testing, design, and field installation and operations. This paper presents a reliability analysis to estimate the deflection (cross-sectional ovalization) and buckling response of buried flexible pipes, considering uncertainties in the design parameters. The need to consider variations in design parameters, such as soil modulus and bulk density of the fill, and the influence of correlation between soil modulus and bulk density in the estimation of reliability is emphasized. It was observed that reliability index decreases with an increase in the coefficient of variation of soil modulus and bulk density of the fill and increases with increase in correlation coefficient between the variables. It is possible to obtain a central factor of safety (CFS) value on the basis of the target reliability and variations in the design parameters. The use of reliability-based considerations is illustrated with two typical simple cases of buried pipe installations.Key words: reliability measures, buried flexible pipes, deflection (ovalization), buckling, variability, safety.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.211
Teacher spread0.201 · 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.

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

Citations15
Published2005
Admission routes1
Has abstractyes

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