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Record W4220713244 · doi:10.1061/jpeodx.0000351

Characteristics of Resilient Modulus of Weathered Phyllite Subgrade during Saturation Process

2022· article· en· W4220713244 on OpenAlexaff
Ying Zhao, Xuesong Mao, M. Hesham El Naggar, Wenlin Li

Bibliographic record

VenueJournal of Transportation Engineering Part B Pavements · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsWestern University
Fundersnot available
KeywordsPhylliteGeotechnical engineeringGradationSaturation (graph theory)Materials scienceSubgradeModulusWater contentComposite materialCompactionGeologyMathematics

Abstract

fetched live from OpenAlex

The gradation and water content have a significant influence on the resilient modulus of strongly weathered phyllite subgrade. In this study, small-scale plate loading tests were conducted on large cylindrical specimens 600 mm in diameter and 450 mm high during the saturation process. The specimens comprised strongly weathered phyllite with varying rock content (0%, 35%, 55%, and 75%). The volumetric water content of each specimen was monitored continuously during the tests. The results demonstrated that the moisture migration rate was largest during the first 5 days of saturation, and the resilient modulus decreased sharply. This was followed by a slight increase in the resilient modulus during the next 10 days of saturation owing to the optimal structure induced by the test loading condition. The results also demonstrated that the resilient modulus of the coarser-grained specimen decreased slightly during the saturation process. A numerical model was established to analyze the stress state of asphalt pavement structure considering the reduction in subgrade resilient modulus. The results obtained from the numerical model indicated that the reduction in subgrade strength could lead to a significant increase of pavement deformation and shear stress.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.653

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 head0.219
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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