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Record W2994873160 · doi:10.1139/cgj-2018-0838

Centrifuge modelling of uplift response of suction caisson groups in soft clay

2019· article· en· W2994873160 on OpenAlexvenueno aff
Bin Zhu, Jialin Dai, Deqiong Kong, Lingyun Feng, Yunmin Chen

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsCaissonGeotechnical engineeringCentrifugeGeologyEngineeringStructural engineering

Abstract

fetched live from OpenAlex

This paper describes a program of centrifuge model tests on the uplift behaviour of suction caisson foundations. The parameters considered were the loading rate, caisson diameter (D), soil strength profile, and type of footing (i.e., mono-caisson and tetra-caissons group). The loading responses were examined in terms of total uplift resistance, suction beneath the caisson lid, and the vertical displacements of the caisson and at the soil surface. There exists a critical uplift displacement, approximately 0.02D and 0.01D for the mono-caisson and the tetra-caissons groups, respectively, at which a turning point can be identified in the load–displacement curve. This was found to be attributed to the adhesion on the caisson–soil interface reaching a peak response and then dropping. Of interest is that the tetra-caissons group exhibits much greater normalized uplift resistance than the mono-caisson group before reaching an uplift displacement of about 0.02D, suggesting superiority of the former in term of serviceability. However, a reversed trend was observed at greater displacement, and accordingly an empirical model was derived to quantify the shadowing effect of caisson groups. Regarding the cyclic response, several cycles of large-amplitude loading are sufficient to reduce the ultimate bearing capacity of caisson(s) to below the self-weight of the inner soil plug(s), indicating a transition of failure mechanism.

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 score0.074
Threshold uncertainty score0.674

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 integrity0.0000.001
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 head0.009
GPT teacher head0.181
Teacher spread0.172 · 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

Citations47
Published2019
Admission routes1
Has abstractyes

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