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Record W2060016585 · doi:10.1520/gtj103324

Simple Techniques for the Estimation of Suction in Compacted Soils in the Range of 0 to 60,000 kPa

2011· article· en· W2060016585 on OpenAlexaff
Sai K. Vanapalli, Won Taek Oh

Bibliographic record

VenueGeotechnical Testing Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGeotechnical engineeringSuctionSoil waterRange (aeronautics)GeologySoil scienceEnvironmental scienceEngineeringMaterials scienceComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Two simple techniques are proposed in this Technical Note to estimate suction values over a range of 0 to 60,000 kPa on compacted glacial till specimens. The first technique uses a conventional pocket penetrometer in the estimation of matric suction values lower than 300 kPa. This technique is developed based on the assumption that there is a strong relationship between matric suction and the compressive strength measured using a pocket penetrometer. The second technique uses conventional tensiometer to estimate relatively high suction values in the range of 1200 to 60,000 kPa. In this technique, the tensiometer response versus time (TRT) behavior for a suction range of 0 to 50 kPa is used in a hyperbolic model to estimate the high suction value. This technique is proposed based on the assumption that each suction value has a unique initial tangent of TRT behavior. The equilibrium suction values of the compacted glacial till specimens are respectively measured using axis-translation technique and a psychrometer for low and high suction values and compared with those estimated using the techniques proposed in this study. There is a reasonably good comparison between the measured and estimated suction values both in the low and high suction range.

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 score0.241
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.055
GPT teacher head0.272
Teacher spread0.218 · 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

Citations11
Published2011
Admission routes1
Has abstractyes

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