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Record W4280576205 · doi:10.1680/jphmg.21.00066

Reduction of footprint problems on spudcan in sand with infilling method

2022· article· en· W4280576205 on OpenAlexaboutno aff
Yung‐Show Fang, Ying‐Chu Shih, Cheng Liu, Jyun-Yi Hsieh

Bibliographic record

VenueInternational Journal of Physical Modelling in Geotechnics · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeologySeabedGeotechnical engineeringFootprint

Abstract

fetched live from OpenAlex

This paper presents experimental data associated with the reactions on the spudcan during the reinstallation of a jack-up foundation near a footprint with and without infilling. All experiments mentioned in this paper were conducted in a 1g model spudcan testing facility. Loose Ottawa sand was used as the seabed and infilling material, and the diameter D of the conical model spudcan was 200 mm. Based on the experimental data, it was found that, with the infilling of the footprint crater, a flat bearing surface was generated on the seabed; therefore, the hazardous stamp-on-void situation was prevented. This was the major contribution of the infilling method. At the small reinstallation depth of 0.05D, between the small offsets 0 and 0.25D, the stamp-on-void condition was eliminated by infilling, and the infilling effect was especially significant. At the large offset 2.0D, since the reinstallation was conducted far from the influence of the first penetration and the footprint crater, the infilling effect nearly vanished. The effect of infilling decreased with increasing reinstallation depth and increasing offset distance.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.464

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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