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Record W2611252235 · doi:10.1139/cgj-2016-0564

Soil restraints on buried pipelines subjected to reverse-fault displacement

2017· article· en· W2611252235 on OpenAlexaffvenue
Dharma Wijewickreme, Manuel Monroy, Douglas G. Honegger, Douglas J. Nyman

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsGolder Associates (Canada)University of British Columbia
Fundersnot available
KeywordsTrenchGeotechnical engineeringOblique casePipeline transportGeologyDisplacement (psychology)Nonlinear systemPipeline (software)Settlement (finance)Structural engineeringMaterials scienceEngineering

Abstract

fetched live from OpenAlex

The interaction between a buried pipeline and surrounding soil during large ground displacements is typically simulated using numerical nonlinear soil-restraint springs aligned with the longitudinal axis of the pipeline and in the two directions orthogonal to it. There are only very limited experimental data available to characterize the soil springs for simulating pipelines crossing reverse faults where large oblique soil displacements relative to the pipe could occur. Full-scale model testing was undertaken to evaluate this complex soil–pipe interaction problem. The tests simulated the performance of ∼400 mm diameter (nominal pipe size, NPS 16) pipe specimens buried in moist sand and crushed limestone trench backfill. The peak normalized oblique soil restraint (N θ ) values for oblique pipe movement angles (θ), when θ = 0° (horizontal movement) and θ = 90° (vertical movement), estimated based on state-of-practice approaches, were in agreement with those from full-scale testing. The value of N θ was found to depend significantly on the peak friction angle of soil ([Formula: see text]) when θ was closer to 0°, whereas N θ was less sensitive to [Formula: see text] when θ was beyond about 35°. The theoretical values of N θ based on limit-equilibrium approaches compared well with the experimental findings.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.229
Teacher spread0.218 · 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

Citations31
Published2017
Admission routes2
Has abstractyes

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