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Record W1934855015 · doi:10.1139/cgj-2013-0110

Effect of perforations on uplift capacity of skirted foundations on clay

2013· article· en· W1934855015 on OpenAlexvenueno aff
Xiaojun Li, Christophe Gaudin, Yinghui Tian, Mark Cassidy

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersLloyd's Register
KeywordsGeotechnical engineeringGeologySuctionPerforationCentrifugeFoundation (evidence)Pore water pressureEffective stressEngineering

Abstract

fetched live from OpenAlex

The retrieval of deep water subsea installations resting on soft soil, such as “mudmat” shallow foundations, can be a difficult and costly operation if significant resistance to uplift is experienced. At the mudmat invert, suctions may develop, increasing the uplift resistance to greater than the weight of the mat. In this paper, a series of centrifuge model tests are performed to determine the uplift resistance of rectangular mudmats resting on lightly overconsolidated kaolin clay. The study investigates the influence of perforation, in combination with skirt length and eccentric uplift, on the uplift resistance and suction generation at the foundation invert. The outcomes demonstrate that the central and eccentric uplift of mudmats have different failure mechanisms, resulting in a different distribution of excess pore pressure at the foundation invert. In contrast, perforations do not change the failure mechanism and only alter the magnitude of suction generated. The two different configurations of perforation investigated significantly reduce the suction at the mat invert and the uplift resistance, and may potentially shorten the operating time for centred uplift. The combination of perforation and eccentric uplift has the most beneficial effect on the reduction of the uplift resistance.

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 score0.107
Threshold uncertainty score0.598

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations32
Published2013
Admission routes1
Has abstractyes

Explore more

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