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Record W2806492195 · doi:10.1061/9780784481486.044

Downhole Seismic Testing within Existing Steel Cased Sonic Boreholes

2018· article· en· W2806492195 on OpenAlexaff
Viji Fernando, Yannick Wittwer, Rob Luzitano, Trevor Fitzell

Bibliographic record

VenueGeotechnical Earthquake Engineering and Soil Dynamics V · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsCasingBoreholeGroutGeologySonic loggingGeotechnical engineeringDrillingStandard penetration testShear (geology)Cone penetration testEngineeringPetroleum engineeringLiquefactionPetrologyMechanical engineering

Abstract

fetched live from OpenAlex

Downhole seismic (DHS) data was reliably recorded in steel cased sonic drilled boreholes without the usual additional time or materials required to grout and prepare the boreholes. DHS results compared well with adjacent seismic cone penetration test (SCPT) results (sonic holes were located within a few metres (m) of existing SCPTs). This technique works well in uncemented granular and fine-grained soils when sufficient time is allowed for the induced dynamic effects to dissipate. However, a drawback of this technique is that a rapid assessment of the data is necessary to ensure that sufficient records have been collected for a complete profile. Once the casing has been pulled the opportunity to collect data is lost, unlike a grouted casing installation that can be revisited to complete additional testing, if necessary. As sonic drilling is becoming more common in geotechnical investigations, this paper demonstrates how sonic holes can be utilized without the added expense and time of a grouted installation to obtain shear wave velocity (Vs) measurements.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.122
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.199
Teacher spread0.189 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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