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Record W2773052992 · doi:10.1139/cgj-2017-0332

Thermal effects on yielding and wetting-induced collapse of recompacted and intact loess

2017· article· en· W2773052992 on OpenAlexvenueno aff
C.W.W. Ng, Qing Cheng, Chao Zhou

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsWettingSofteningLoessMaterials scienceGeotechnical engineeringComposite materialGeology

Abstract

fetched live from OpenAlex

Yielding and wetting-induced collapse are two important interrelated aspects of unsaturated loess behaviour. Previous studies on loess were generally conducted under a single temperature condition. The principal objective of this study is to investigate thermal effects on yielding and wetting-induced collapse of recompacted and intact loess. Isotropic compression tests were carried out to determine yield stress at different suctions (0 and 100 kPa) and temperatures (5, 23, and 50 °C). Moreover, wetting tests were conducted at various temperatures and stresses. Results of the wetting tests were interpreted using the measured yield stress at various suctions and temperatures. It is found that yield stress decreases with decreasing suction (wetting-induced softening). The wetting-induced softening of recompacted loess is more significant at a higher temperature. The observed thermal effects on wetting-induced softening are likely because with decreasing suction, the stabilizing interparticle normal force decreases more at a higher temperature. In contrast, when the applied stress reaches the yield stress during wetting, yielding and plastic volumetric contraction can be observed. More importantly, wetting-induced contraction of recompacted loess at 50 °C is about three times of that at 5 °C. The larger contraction at 50 °C is mainly because the wetting-induced softening is larger at a higher temperature.

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 score0.797
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 integrity0.0000.001
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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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

Citations37
Published2017
Admission routes1
Has abstractyes

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