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Record W4280514559 · doi:10.1139/cgj-2021-0579

Effect of hydraulic conductivity and impeded drainage on the liquefaction potential of gravelly soils

2022· article· en· W4280514559 on OpenAlexvenueno aff
Jashod Roy, Kyle M. Rollins

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic conductivityLiquefactionPore water pressureGeotechnical engineeringImpervious surfaceSoil waterGeologyEffective stressDrainageSoil liquefactionSoil science

Abstract

fetched live from OpenAlex

Investigating the role of sand and fines content and in situ drainage conditions in governing the hydraulic conductivity of gravelly deposits is highly important to characterize the liquefaction potential of gravelly soil. In this study, a variation of hydraulic conductivity with sand content has been empirically obtained based on the existing gravel liquefaction case histories. It is found that the hydraulic conductivity of a soil matrix having more than about 20%–30% sand content by mass is low enough to cause liquefaction without the presence of any impervious confining layer. In addition, a numerical study has been performed using the commercial software FEQDrain to study pore pressure generation in gravelly soil at a variety of relative densities and hydraulic conductivities with and without an impermeable cap layer when subjected to a variety of earthquake loadings. For both unconfined and confined condition, excess pore pressure ratios consistently increase with a decrease in hydraulic conductivity ( k) and relative density ( D r ). Excess pore pressure ratio is correlated with hydraulic conductivity, soil compressibility, and cyclic stress ratio (CSR). For the confined condition, pore pressure in the gravel layer is primarily governed by the overlying cap layer and even a sandy cap layer instead of highly impervious clay layer can cause liquefaction.

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)0.001
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 score0.135
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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