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Record W2158631490 · doi:10.1139/t2012-070

Cyclic axial behaviour of piles and pile groups in sand

2012· article· en· W2158631490 on OpenAlexvenueno aff
Zheming Li, M. D. Bolton, Stuart K. Haigh

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersCambridge TrustChina Scholarship CouncilCambridge Overseas Trust
KeywordsPileCentrifugeGeotechnical engineeringStiffnessStructural engineeringAxial symmetrySettlement (finance)EngineeringDynamic load testingStructural loadGeology

Abstract

fetched live from OpenAlex

Piled foundations are often subjected to cyclic axial loads. This is particularly true for the piles of offshore structures, which are subjected to rocking motions caused by wind or wave actions, and for those of transport structures, which are subjected to traffic loads. As a result of these cyclic loads, excessive differential or absolute settlements may be induced during the piles’ service life. In the research presented here, centrifuge modelling of single piles and pile groups was conducted to investigate the influence of cyclic axial loads on the performance of piled foundations. The influence of installation method was investigated and it was found that the cyclic response of a pile whose jacked installation was modelled correctly is much stiffer than that of a bored pile. During displacement-controlled axial load cycling, the pile head stiffness reduces with an increasing number of cycles, but at a decreasing rate; during force-controlled axial load cycling, more permanent settlement is accumulated for a bored pile than for a jacked pile. The performance of individual piles in a pile group subjected to cyclic axial loads is similar to that of a single pile, without any evident group effect. Finally, a numerical analysis of axially loaded piles was validated by centrifuge test results. Cyclic stiffness of soil at the base of pre-jacked piles increases dramatically, while at base of jacked piles it remains almost constant.

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: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.620

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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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

Citations33
Published2012
Admission routes1
Has abstractyes

Explore more

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