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Record W1812667501 · doi:10.1139/cgj-2013-0438

Effect of variation of the loading direction on the displacement accumulation of large-diameter piles under cyclic lateral loading in sand

2014· article· en· W1812667501 on OpenAlexvenueno aff
Christina Rudolph, Britta Bienen, Jürgen Grabe

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersAustralian Research CouncilDeutsche Forschungsgemeinschaft
KeywordsCentrifugeGeotechnical engineeringDisplacement (psychology)Structural engineeringTransverse planeEmbedmentDeformation (meteorology)PileStress (linguistics)GeologyFoundation (evidence)Engineering

Abstract

fetched live from OpenAlex

Foundation piles supporting offshore structures experience cyclic lateral loading arising from waves and wind, which are not typically uni-directional over the lifetime of the structure. This paper presents results from centrifuge experiments in sand, representing a large diameter prototype tested at stress levels similar to the field subjected to cyclic lateral loading from varying directions. The results demonstrate increased deformation accumulation due to the changing loading direction, compared to the uni-directional case. Displacement accumulation is not limited to the main loading direction but includes transverse movement as well. Similar trends were observed in small-scale 1g modelling that allowed a larger number of load cycles to be applied. The centrifuge test results provide confidence of the applicability of the findings to the prototype. Current methods that neglect the effect of variation of the loading direction will provide predictions of displacement accumulation of piles that are un-conservative. Therefore, a simple approach is proposed here to estimate the augmentation of displacement accumulation due to variation in loading direction compared to the uni-directional case.

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 categoriesnone
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 score0.050
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations63
Published2014
Admission routes1
Has abstractyes

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