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Record W2555515813 · doi:10.1139/cgj-2016-0111

Assessing the penetration resistance acting on a dynamically installed anchor in normally consolidated and overconsolidated clay

2016· article· en· W2555515813 on OpenAlexvenueno aff
Colm O’Beirne, Conleth O’Loughlin, Christophe Gaudin

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersAustralian Research CouncilLloyd's Register
KeywordsEmbedmentGeotechnical engineeringCentrifugeDragShearing (physics)PenetrometerGeologyStructural engineeringEngineeringSoil water

Abstract

fetched live from OpenAlex

Predicting the final embedment depth of a dynamically installed anchor is a key prerequisite for reliable calculation of anchor capacity. This paper investigates the embedment characteristics of dynamically installed anchors in normally consolidated and overconsolidated clay through a series of centrifuge tests involving a model anchor instrumented with a microelectric mechanical system (MEMS) accelerometer, enabling the full motion response of the anchor to be established. The data are used to assess the performance of an anchor embedment model based on strain-rate-dependent shearing resistance and fluid mechanics drag resistance. Predictions of a database of over 100 anchor installations — formed from this study and the literature — result in calculated anchor embedment depths that are within ±15% of the measurements. An interesting aspect, consistent across the entire database, relates to the strain rate dependence on frictional resistance relative to bearing resistance. The predictions reveal that strain rate dependency may indeed be higher for frictional resistance, although only if a soil strength lower than the fully remoulded strength is considered as the reference strength, which suggests that water may be entrained along a boundary layer at the anchor–soil interface during installation.

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)0.001
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 score0.393
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

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