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Record W7046938406

Evaluation of pipeline performance subjected to slope instabilities

2021· dissertation· en· W7046938406 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEmbedmentParametric statisticsPipeline (software)LandslideDimensionless quantityPipeline transportFinite element method
DOInot available

Abstract

fetched live from OpenAlex

The horizontal soil-pipe interaction in slopes is characterized in this research program for inclusion in pipeline guidelines. For this purpose, a series of full-scale experiments were conducted at the Advanced Soil-Pipe Interaction Research (ASPIRe™) testing facility at the University of British Columbia, Vancouver, BC, Canada. The experimental data indicated that the soil load is an increasing function of the slope grade for soil springs inside the landslide boundaries and a decreasing function of the slope grade for soil springs outside the landslide boundaries. The lateral force-displacement responses of pipes installed below sloping ground were presented and compared to those arising from the level ground condition. The experimental results suggest that the values of the horizontal bearing capacity factor can be two-fold higher than those estimated using pipeline guidelines. A finite element model was calibrated against the experimental data and was implemented in an extensive parametric study to extend the results to deep embedment conditions for loose, medium, and dense sands. The horizontal bearing capacity factors are presented in dimensionless graphs as a function of the slope grade and pipe burial depth, which can be used in pipeline guidelines as a benchmark for the design.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.954

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 integrity0.0000.000
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 head0.023
GPT teacher head0.237
Teacher spread0.213 · 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 designQualitative
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
Published2021
Admission routes2
Has abstractyes

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