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Record W2773240411 · doi:10.1139/cgj-2017-0321

Assessment of rock strength from measuring while drilling shafts in Florida limestone

2017· article· en· W2773240411 on OpenAlexvenueno aff
Michael Rodgers, Michael McVay, David Horhota, José Ignacio Hernando

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersFlorida Department of Transportation
KeywordsDrillingDrillRate of penetrationGeotechnical engineeringCompressive strengthDrilling fluidGeologyDrilling rigMeasurement while drillingDirectional drillingPetroleum engineeringEngineeringMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

The focus of this research is the real-time assessment of rock strength (unconfined compressive strength, q u ) during drilled shaft installations in Florida limestone, where measures of rock strength are provided through five monitored drilling parameters: torque, crowd, rotational speed, penetration rate, and bit diameter. To complete the study, both a laboratory and field investigation were required. This paper covers drill rig instrumentation, measuring rock strength during field drilling, and the comparative analysis of rock strength with conventional methods. Real-time measurements were recorded for each drilling parameter and graphically displayed on an in-cab monitor and wirelessly transmitted to an external computer. Measures of rock strength were estimated using a laboratory-developed equation with the monitored drilling parameters for real-time field assessment. Measuring while drilling in the field took place at three separate locations where drilled shaft load testing occurred. Comparative analyses between the monitored shaft installations and core samples subjected to unconfined compression indicated the results aligned well when recoveries were good. As recoveries diminished, the mean strengths were comparable, but more variable.

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 score0.072
Threshold uncertainty score0.996

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 science0.0010.000
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 head0.018
GPT teacher head0.225
Teacher spread0.208 · 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
Published2017
Admission routes1
Has abstractyes

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