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Record W1973127276 · doi:10.1139/t07-020

Seismic monitoring short-duration events: liquefaction in 1<i>g</i>models

2007· article· en· W1973127276 on OpenAlexvenueno aff
Jong‐Sub Lee, J. Carlos Santamarina

Bibliographic record

VenueCanadian Geotechnical Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersUniversity of California, DavisNational Science Foundation
KeywordsLiquefactionPore water pressureGeologyGeotechnical engineeringEffective stress

Abstract

fetched live from OpenAlex

The duration of liquefaction in small models is very short, therefore special monitoring systems are required. In an exploratory sequence of liquefaction tests, S-wave transillumination is implemented with a high repetition rate to provide detailed information on the evolution of shear stiffness during liquefaction. These data are complemented with measurements of acceleration, time-varying settlement, excess pore pressure, and resistivity profiles. Measurements show that excess pore pressure migration from liquefied deep layers may cause or sustain a zero effective stress condition in shallow layers, that multiple liquefaction events may take place in a given formation for a given excitation level, and that unsaturated layers may also reach a zero effective stress condition. The time scale for excess pore pressure dissipation in fully submerged specimens is related to particle resedimentation and pressure diffusion; downward drainage from unsaturated shallow layers may contribute an additional time scale. High resolution resistivity profiling reveals the gradual homogenization of the soil bed that takes place during subsequent liquefaction events. The S-wave transillumination technique can be extended to field applications and implemented with tomographic coverage to gain a comprehensive understanding of the spatial and temporal evolution of liquefaction.Key words: densification, electrical resistivity, multiple liquefaction, pore pressure, shear wave, spatial variability.

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 score0.498
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.219
Teacher spread0.204 · 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
Published2007
Admission routes1
Has abstractyes

Explore more

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