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Record W2560359359 · doi:10.1139/cgj-2016-0347

Compression and shear strength characteristics of compacted loess at high suctions

2016· article· en· W2560359359 on OpenAlexvenueno aff
Charles Wang Wai Ng, Hamed Sadeghi, Fardin Jafarzadeh

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringDilatantShearing (physics)Materials scienceSuctionWater contentWettingShear stressSaturation (graph theory)LoessShrinkageComposite materialGeologyMathematicsEngineering

Abstract

fetched live from OpenAlex

Compression and shear behavior of a loosely compacted loess is investigated via two series of saturated and unsaturated direct shear tests. The vapor transfer mechanism is used to modify a shear box device for control of suction at high range. In spite of significant volumetric strain upon wetting (up to 14%), suction-induced volumetric shrinkage is less than 2% for all suction levels considered because of the as-compacted moisture content at the dry side of the optimum value. During shearing, all unsaturated tests dried to high suctions indicate a strain-softening mode of failure associated with noticeable dilation. There is a continuous increase in peak strength with suction but at a reduced rate, which cannot be captured by the improved Bishop’s effective stress model, as it underestimates the contribution of high suctions by approaching zero degree of saturation. Unsaturated tests at high suctions also show an increased rate of dilation with suction for both values of net stress, which cannot be predicted well by the classical stress–dilatancy models. It is shown that for suction values beyond 8 MPa, dilation angle increases by 2° and 6° per 100 MPa increase of suction under net stress of 50 and 200 kPa, respectively.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.291

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 head0.008
GPT teacher head0.188
Teacher spread0.181 · 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 designObservational
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

Citations84
Published2016
Admission routes1
Has abstractyes

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