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Record W1942431850 · doi:10.1139/t2012-037

Investigation of the vertical uplift capacity of deep water mudmats in clay

2012· article· en· W1942431850 on OpenAlexvenueno aff
Ruilin Chen, Christophe Gaudin, Mark Cassidy

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersAustralian Research CouncilDalian University of TechnologyNational Natural Science Foundation of China
KeywordsGeologyGeotechnical engineeringCentrifugeSubseaSuctionPore water pressureSeabedSubmarine pipelineEffective stressBearing capacityOffshore geotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

Subsea mudmats are used in deep water oil and gas fields to support seabed structures, such as pipelines, manifolds, and well heads. On soft soil, removing mudmats during maintenance or decommissioning may be difficult and costly due to the significant suction that develops at the mudmat–soil interface, considerably increasing the uplift resistance beyond the submerged self-weight of the mudmat. This paper describes a series of centrifuge tests performed to investigate the uplift resistance of plain mudmats resting on lightly overconsolidated clay. The model mudmat invert was instrumented with pore pressure transducers to monitor the suction developing at the mudmat invert at various uplift velocities, uplift eccentricities, and mudmat skirt lengths. The results show an increase in uplift resistance with pull-out velocity and the dominant role played by the uplift eccentricity in reducing suction at the mat invert. Simple predictive methods based on bearing capacity theory and combined loading interaction diagrams are provided.

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 score0.639
Threshold uncertainty score0.310

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 head0.181
Teacher spread0.170 · 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

Citations39
Published2012
Admission routes1
Has abstractyes

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