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Record W2999736249 · doi:10.1139/cgj-2019-0066

The changing strength of carbonate silt: parallel penetrometer and foundation tests with cyclic loading and reconsolidation periods

2020· article· en· W2999736249 on OpenAlexvenueno aff
Zefeng Zhou, David White, Conleth O’Loughlin

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsPenetrometerGeotechnical engineeringConsolidation (business)CentrifugeSiltPore water pressureGeologyFoundation (evidence)Soil waterBearing capacitySoil science

Abstract

fetched live from OpenAlex

This paper describes a centrifuge study using novel penetrometer tests (T-bar and piezoball) and model foundation tests to explore through-life changes in the strength of a reconstituted natural carbonate silt. The test procedures include episodic cyclic loading, which involves intervals of pore pressure dissipation between cyclic packets. These loads and the associated remoulding and reconsolidation cause significant changes in the soil strength and foundation capacity. Soil strength changes from penetrometer tests differed by a factor of 15 from the fully remoulded strength to a limiting upper value after long-term cyclic loading and reconsolidation. For the model foundation tests, the foundation capacity of a surface foundation and a deep-embedded plate were studied. The soil strength interpreted from the measured foundation capacity varied by a factor of up to three due to episodes of loading and consolidation, with an associated order of magnitude increase in the coefficient of consolidation. The results show a remarkable rise in soil strength over the loading events and provide a potential link between changes in soil strength observed in penetrometer tests and the capacity of foundations, allowing the effects of cyclic loading and consolidation to be predicted.

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 score0.300
Threshold uncertainty score0.435

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 integrity0.0000.000
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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

Explore more

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