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Record W2171108389 · doi:10.1139/cgj-2014-0356

Experimental and numerical study on determination of indirect (splitting) tensile strength of rocks under various load apparatus

2015· article· en· W2171108389 on OpenAlexvenueno aff
Eren Kömürlü, Ayhan Kesimal, Serhat Demi̇r

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsUltimate tensile strengthMaterials scienceFracture toughnessCrackingToughnessComposite materialCompressive strengthFinite element methodFracture (geology)Geotechnical engineeringStructural engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

The indirect tensile strengths (ITSs) of more than 200 disc specimens for 19 different rock and rock-like materials were determined by considering the concept of fracture toughness under different loading conditions. Steel loading arcs with various contact angles and flat platens were used in experiments to investigate whether they can be used as alternatives for the standard Brazilian jaw. Many of the disc specimens tested under the standard jaw did not fail with ideal tensile cracking at the centre, whereas simple splitting cracks were obtained along the vertical diameter of most of the discs tested under the flat platens. However, flat platens caused cracks to initiate in the compressive zone just under the loading points. This is an important outcome for criticizing the validity of the test because tensile failure is expected to occur along the vertical diameter of the disc. Friction conditions between the disc and jaw have important roles in the failing load. Moreover, fracture toughness is an additional parameter for investigating the ITSs of rock materials. According to the experimental results, fracture toughness values were found to increase with an increase in contact angle. In addition to the experimental study, finite element analyses were performed on several types of rock discs under various loading conditions. As a result, a comparison between all loading conditions was made to determine the best ITSs of the rocks. Both experimental and numerical analyses concluded that deciding the best loading type depends on the rock type. Therefore, the standard jaw is not recommended to use for determination of the ITS values of all rock types.

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 score0.083
Threshold uncertainty score0.469

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 head0.250
Teacher spread0.227 · 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

Citations38
Published2015
Admission routes1
Has abstractyes

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