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Record W3024298523 · doi:10.1139/cgj-2019-0446

Downwards force–displacement response of buried pipelines during dip–slip faulting in sandy soil

2020· article· en· W3024298523 on OpenAlexvenueno aff
Hamid Tohidifar, Mohammad Kazem Jafari, Mojtaba Moosavi

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersInternational Institute of Earthquake Engineering and SeismologyVirginia Polytechnic Institute and State University
KeywordsGeotechnical engineeringGeologyCompressibilitySlip (aerodynamics)StiffnessPipeline transportBearing (navigation)Nonlinear systemDisplacement (psychology)Structural engineeringEngineering

Abstract

fetched live from OpenAlex

In the numerical analysis of underground pipelines against dip-slip faulting, it is common practice to model the problem as a beam surrounded by soil-equivalent nonlinear springs. Previous studies have recognised the importance of the vertical bearing soil springs (VBSS) in the modelling of pipe–fault interaction. Nevertheless, prior studies on VBSS are limited. This study presents an analytical framework for determining a hyperbolic force–displacement (P–y) curve for VBSS in dry sand. This hyperbolic criterion was established based on the ultimate bearing resistance (UBR) of pipe and the elastic subgrade modulus of the soil. Different mechanisms such as flow, arching, bearing, and friction, and various parameters like soil compressibility, pipe–soil relative stiffness, and soil elastic properties have been used in the determination of these two parameters. The P–y curves were verified and calibrated with experimental data. More reliable curves have been obtained compared to the existing methods. A numerical study was also performed to compare the current study with a pipeline seismic design guideline. The results of the current study method are in good agreement with sample experimental data compared to the seismic guideline.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
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 studies0.0000.000
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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

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