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Record W2887778802 · doi:10.1139/cgj-2017-0647

Effect of perforations on the bearing capacity of shallow foundation on clay

2018· article· en· W2887778802 on OpenAlexvenueno aff
Run Liu, Meng-meng Liu, Ying-hui Tian, Xinli Wu

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsShallow foundationGeotechnical engineeringSubseaBearing capacityFoundation (evidence)PerforationGeologyDimensionless quantityWaves and shallow waterSeabedMaterials scienceMechanicsComposite materialPhysics

Abstract

fetched live from OpenAlex

As a type of shallow foundation, a mudmat serves as the seabed support structure for subsea wells, pipeline manifolds, and pipeline terminations. The shallow foundations are usually designed with perforations to facilitate installation and removal, but the influence of these perforations has not been fully understood. This paper presents a method to analyze the bearing capacities of both two-dimensional (2D) and three-dimensional (3D) perforated shallow foundations using finite element analysis. The soil was idealized as a Tresca material, with the undrained strength increasing linearly with depth. The outcome indicates that perforations have nonnegligible effects on the bearing capacity of shallow foundations. The bearing capacity decreases with increasing perforation ratio, R, and the degree of reduction increases with the increase of the dimensionless ratio kB/S uo , where k is the shear strength gradient, B is the width of the foundation, and S uo is the shear strength at the mudline. For 2D shallow foundations, there exists a critical perforation ratio, R c ; when the perforation ratio is lower than the critical perforation ratio, the perforated foundation does not lose its bearing capacity. For 3D shallow foundations, the bearing capacity decreases directly with the increase of perforation ratio, R.

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 score0.140
Threshold uncertainty score0.379

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 head0.211
Teacher spread0.200 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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