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Postcyclic Reconsolidation Strains in Low-Plastic Fraser River Silt due to Dissipation of Excess Pore-Water Pressures

2010· article· en· W1986049630 on OpenAlexafffund
Dharma Wijewickreme, Maria V. Sanín

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSiltPore water pressureVoid ratioGeotechnical engineeringMaterials scienceComposite materialGeology

Abstract

fetched live from OpenAlex

The postcyclic reconsolidation response of low-plastic Fraser River silt was examined using laboratory direct simple shear testing. Specimens of undisturbed and reconstituted natural low-plastic Fraser River silt and reconstituted quartz powder, initially subjected to constant-volume cyclic loading under different cyclic stress ratios (CSRs) and then reconsolidated to their initial effective stresses (σvo′), were specifically investigated. The volumetric strains during postcyclic reconsolidation (εv-ps) were noted to generally increase with the maximum cyclic excess pore-water pressure (Δumax) and maximum cyclic shear strain experienced by the specimens during cyclic loading. The values of εv-ps and maximum cyclic excess pore-water pressure ratio (ru-max) were observed to form a coherent relationship regardless of overconsolidation effects, particle fabric, and initial (precyclic) void ratio of the soil. The specimens with high ru-max suffered significantly higher postcyclic reconsolidation strains; εv-ps ranging between 1.5 and 5% were noted when ru-max>0.8. The observed εv-ps versus ru-max relationship, when used in combination with the observed dependence of cyclic excess pore-water pressure on CSR and number of load cycles, seems to provide a reasonable approach to estimate postcyclic reconsolidation strains of low-plastic silt.

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 score0.140
Threshold uncertainty score0.685

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 integrity0.0000.001
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 head0.003
GPT teacher head0.174
Teacher spread0.171 · 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

Citations39
Published2010
Admission routes2
Has abstractyes

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