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Record W2411035887 · doi:10.1139/cgj-2016-0015

Development of global correlation models between in situ stress-normalized shear wave velocity and soil unit weight for plastic soils

2016· article· en· W2411035887 on OpenAlexvenueno aff
Sung-Woo Moon, Taeseo Ku

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersMinistry of Education, India
KeywordsVoid ratioGeotechnical engineeringSoil waterPlasticityShear stressShear (geology)Effective stressWave velocityGeologyMathematicsSoil scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Shear wave velocity (Vs) in geo-materials is strongly dependent on factors such as stress state, void ratio, and soil structure. Stress-dependency and void-ratio dependency can be represented by the equations [Formula: see text] and Vs = a(e)b (where α and a are material constants; exponents β and b represent the sensitivity of stress and the void dependent effect, respectively; [Formula: see text] is effective confining stress; e is void ratio), respectively. To consider the effect of soil disturbance and stress relief in geo-materials, shear wave velocity is often required to be normalized by adopting the site-specific model parameters (β or b). Based on a special in situ database compiled from 156 well-documented test sites that include various geo-materials, this study presents (i) the apparent relationships of the model parameters α and β for all soil and rock materials as well as a and b for all soil materials, (ii) new global correlations between soil unit weight and two types of stress-normalized shear wave velocities (Vs1 and Vsn), instead of the conventional Vs – soil unit weight relationship for clays, and (iii) the best-fitted multi-regression models between soil unit weight and site-specifically normalized shear wave velocity as well as the plasticity index for plastic soils. Moreover, this study presents the importance of site-specific stress normalization (Vsn) in creating a better correlation model. The proposed relationships offer first-order assessments of soil unit weight within the ranges of available data, which are also approximately guided by a hyperbolic unit weight model with depth.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.205
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2016
Admission routes1
Has abstractyes

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