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Record W2767439544 · doi:10.1139/cgj-2016-0522

Small-strain behaviour and crushability of Ballyconnelly carbonate sand under monotonic and cyclic loading

2017· article· en· W2767439544 on OpenAlexvenueno aff
Satyajeet Nanda, V. Sivakumar, Shane Donohue, Sean A. Graham

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsBreakageGeotechnical engineeringGeologyCarbonateSoil waterShear (geology)Submarine pipelineMaterials scienceSoil scienceComposite material

Abstract

fetched live from OpenAlex

In various parts of the globe, carbonate sands are found at shallow sea water depth. These types of sands are very susceptible to large-scale particle breakage. Offshore structures like wind turbines and sea defences are constructed on these types of soils. From a design perspective, it is essential to assess the extent of particle breakage and the subsequent change in soil properties that occur under working load conditions. This paper presents the data obtained from a number of drained monotonic and cyclic triaxial tests on crushable carbonate sand (“Ballyconnelly sand”) in conjunction with small-strain shear stiffness (G max ) measurements using the bender element technique. The soils were allowed to shear under three different loading patterns to understand the factors influencing the breakage of particles. The degree of crushing was quantified and analysed based on the total energy input. It was observed that, apart from applied stress, the total strain accumulation governs the amount of particle breakage. It was observed that G max increased significantly under high stress ratio. G max also increased noticeably during resting periods without any change in loading conditions as a result of creep, and subsequently during cyclic loading although at a reduced rate.

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 score0.317
Threshold uncertainty score0.902

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 head0.015
GPT teacher head0.204
Teacher spread0.188 · 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

Citations30
Published2017
Admission routes1
Has abstractyes

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