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Record W2076747462 · doi:10.1680/geng.2006.159.2.99

Sample disturbance effects on medium plasticity clay/silt

2006· article· en· W2076747462 on OpenAlexaboutno aff
Michael Long

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Geotechnical Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsSiltGeotechnical engineeringStiffnessPlasticitySoil waterGeologySampling (signal processing)Tube (container)Materials scienceEnvironmental scienceEngineeringSoil scienceStructural engineeringComposite material

Abstract

fetched live from OpenAlex

Practising engineers designing civil engineering works on soft clay usually assume that laboratory tests on normal piston tube sampling will at worst give conservative design parameters. Indeed most research into sampling disturbance effects on soft clays has proven that use of poor-quality tube samplers ultimately leads to lower undrained strength, stiffness and preconsolidation stress than actually exist in situ. There is some evidence to suggest that this may not hold true for ‘intermediate’ clay/silts or laminated soils. These issues were investigated for the Athlone laminated clay/silt, and it was found that tube sampling disturbance leads to increase in stiffness and undrained strength, and induces a strong tendency for dilatant behaviour post peak. These findings were made by comparing tube sample data with data from high-quality Sherbrooke block sampling and full-scale field trials. From the results of this investigation, and from comparisons with other soils, it is concluded that undrained tube sampling strains have only a minor influence, and that the behaviour of these medium plasticity clay/silts is due mainly to partially drained tube insertion process. Engineers dealing with soft ‘intermediate’ or laminated soils need to assess the laboratory-derived parameters carefully, especially when the clay content is less than about 40% and the plasticity index is less than 20%.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.313
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.169
Teacher spread0.165 · 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.

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

Citations22
Published2006
Admission routes1
Has abstractyes

Explore more

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