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Record W4403461845 · doi:10.1139/cgj-2024-0137

Shear-wave-velocity profiling of a test embankment for a high-speed rail project using the spectral-analysis-of-surface-waves (SASW) method

2024· article· en· W4403461845 on OpenAlexvenueno aff
Gunwoong Kim, Kenneth H. Stokoe, Sungmoon Hwang

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsWave velocityGeotechnical engineeringLeveeGeologyShear (geology)Surface waveStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Nondestructive seismic testing was conducted at 24 locations on and around the embankment to evaluate the compaction effort and lateral variabilities at the site. This embankment was being constructed as a test embankment for a high-speed railroad section in the USA. Different compaction techniques were used in different embankment areas, and nuclear-gauge density measurements were performed to evaluate the compaction quality in all areas. Additionally, Spectral-Analysis-of-Surface-Waves (SASW) testing was performed to determine the Vs profiles at numerous locations on and around the embankment. The Vs values determined from the SASW testing were used to compare and evaluate areas where different compaction methods were used, as well as to help understand the overall site. The embankment was divided into three zones. In each zone, five shorter and one longer SASW arrays on top of the embankment were used to determine the Vs profiles. Also, in each zone, Vs profiles in the natural soil were determined using two additional long SASW arrays on the natural ground, one array on each longitudinal side of the embankment. These 24 Vs profiles on and around the embankment were used to evaluate lateral variability in the shear stiffnesses of the geotechnical materials at the site.

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 score0.158
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.061
GPT teacher head0.340
Teacher spread0.279 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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