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Record W3138621629 · doi:10.1155/2021/6646514

In Situ Frost‐Heaving Characteristics of Shallow Layer of Soil Slopes in Intermittently Frozen Region Based on PFC<sup>3D</sup>

2021· article· en· W3138621629 on OpenAlexaff
Yunbin Ke, Yun Que, Yuanshuai Fu, Zhenliang Jiang, Said M. Easa, Yanyu Chen

Bibliographic record

VenueAdvances in Civil Engineering · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsToronto Metropolitan University
FundersNatural Science Foundation of Fujian ProvinceNational Natural Science Foundation of China
KeywordsFrost heavingGeotechnical engineeringIn situGeologyTension (geology)Materials scienceDisplacement (psychology)MechanicsComposite materialChemistryCompression (physics)

Abstract

fetched live from OpenAlex

The in situ frost‐heaving (FH) process and characteristics of the shallow layer of a residual soil slope in the intermittently frozen zone were simulated by a modified three‐dimensional particle flow code (PFC 3D ) model, of which the mesoscopic parameters of soil and ice particles were calibrated through the indoor experiments. In this model, the in situ FH process was gradually achieved by expanding the volume of ice particles (divided into 24 times of expansion), and the process was terminated when the monitored porosity was stable. These countermeasures avoided the stress accumulation and effectively realized the simulation of the in situ FH process. The results found that the displacement occurred firstly and got the largest final value at the surface angle (SA) under the in situ FH effect, followed by that at the foot, and it gradually extended to the interior based on these two regions. The vertical tension was present at the SA, and the major force type in the lateral interlayer was pressure. In addition, the FH effect seemed to be strongly related to the frozen depth, and a sliding surface was found in a steeper slope. Finally, the smaller stone appeared to be favorable to the slope stability, but it was reduced by the larger stone to some extent.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.972

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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