MétaCan
Menu
Back to cohort
Record W4312293685 · doi:10.1115/omae2022-79227

Indentation Test Implementation for Rock Strength Correlation by Experimental Method and Simulation Using Distinct Element Method

2022· article· en· W4312293685 on OpenAlexaff
Prajit Premraj, Zijian Li, Abdelsalam Abugharara, Stephen Butt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIndentationGeologyDrillingGeotechnical engineeringRock mechanicsStructural engineeringMaterials scienceEngineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Rock strength is an important parameter for the drilling penetration estimation, which is usually determined through standard destructive strength tests, which are known to be sophisticated, expensive, and time-consuming. In addition, the accuracy of these tests has a high standard on the rock sample dimension. As a non-conventional method, the rock indentation test is reviewed and conducted in this study to solve these problems. The sample preparation and test method of the rock indentation test are validated to standardize the test. Based on the stress-strain behavior of the test samples, the correlation between the rock indentation hardness index (IHI) and the rock UCS strength is developed based on different lithology. Then the rock indentation test is simulated in the distinct element method (DEM) in PFC2D software. The robustness of the DEM simulation is validated, and the sample failure pattern is studied. The significance of the process is that it very closely matches with the process a rock fails under the cones of a roller cone drilling bit penetrating the rock.

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: none
Teacher disagreement score0.815
Threshold uncertainty score0.559

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 head0.022
GPT teacher head0.354
Teacher spread0.331 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicTunneling and Rock MechanicsFrench-language works237,207