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Record W4229078347 · doi:10.1139/cgj-2021-0297

Impact of fines on the accumulated strain of unsaturated road base aggregate under cyclic loadings

2022· article· en· W4229078347 on OpenAlexvenueno aff
Zhigang Cao, Qi Zhang, Yuanqiang Cai, Yu‐Jun Cui, Chuan Gu, Jun Wang

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsSuctionGeotechnical engineeringStrain (injury)Materials scienceAggregate (composite)Base (topology)Composite materialGeologyEngineeringMathematics

Abstract

fetched live from OpenAlex

The property of a road base depends on its fine content (FC) and water content/suction. In this study, a cyclic loading test was carried out on road base aggregate with different FCs under controlled suction. The different effects of fines on the accumulated strain of road base aggregate at different FCs and suctions were identified. Under the saturated condition, the accumulated strain increased with FC due to the reduction of soil interparticle friction caused by the inclusion of fines. Under unsaturated conditions, the effect of fines on the accumulated strain depended on the inner structure of the soil. For the “coarse-grain-controlled soil” (FC in the range of 0%–4%), the accumulated strain increased with the increase of FC due to the reduction of interparticle friction, while for the “transition soil” (FC in the range of 4%–8%), the accumulated strain decreased with the increase of FC as the major impact of the fines shifted to the enhancement of suction, and the inclusion of fines is beneficial for this case; for the “fine-grain-controlled soil” (FC in the range of 8%–16%), the accumulated strain increased rapidly with FC as the effect of interparticle friction reduction dominated again.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 spread0.211 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

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