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Record W4392509698 · doi:10.1061/9780784485316.035

A Liquefaction Triggering Surface for Ottawa F65 Sand: Cyclic DSS Results and PM4Sand Calibration

2024· article· en· W4392509698 on OpenAlexaboutno aff
L. Medina Luna, Minyong Lee, Mitchell deJager, Jason T. DeJong, Michael G. Gomez, Katerina Ziotopoulou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsLiquefactionCalibrationGeotechnical engineeringGeologyEnvironmental sciencePetroleum engineeringComputer scienceRemote sensingMaterials scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Liquefaction triggering results from 48 cyclic direct simple shear (DSS) tests are presented for Ottawa F65 sand under constant volume and level ground conditions, at a vertical stress of 100 kPa. An analytical framework is presented to relate relative density (DR), cyclic stress ratio (CSR), and number of cycles to liquefaction (N) using a simple expression with three fitting parameters, which can provide improved interpretation of DSS test results. The expression can be rearranged to be in CSR versus N space, as is traditional when presenting DSS results, or in CSR versus DR space, as is conventional with case history data. The mean liquefaction triggering surface for Ottawa F65 sand is presented together with an estimate of the uncertainty associated with the full experimental dataset. The results are then used to obtain element-level calibrations of the constitutive model PM4Sand across the experimental parameter range that bound the uncertainty from cyclic DSS tests.

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 score0.479
Threshold uncertainty score0.432

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
Published2024
Admission routes1
Has abstractyes

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