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Record W4225116210 · doi:10.3390/ma15093204

Effect of Elevated Temperature on Rhyolitic Rocks’ Properties

2022· article· en· W4225116210 on OpenAlexaff
Haitham M. Ahmed, Mohammed A. Hefni, Hussin A. M. Ahmed, Sefiu O. Adewuyi, Ferri Hassani, Agus P. Sasmito, Hussein A. Saleem, Essam B. Moustafa, Gamal S. A. Hassan

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsMcGill University
FundersKing Abdulaziz University
KeywordsRhyoliteMaterials scienceCompressive strengthComposite materialElastic modulusHardening (computing)MineralogyGeologyVolcanic rock

Abstract

fetched live from OpenAlex

The effect of high temperatures on rock's thermophysical and mechanical properties is critical to the design of underground geotechnical applications. The current work investigates the impact of temperature on rhyolitic turf rock's physical and mechanical properties. Intact cylindrical core rock samples were heated to different temperatures (200, 400, 600, and 800 °C). The uniaxial compressive strength (UCS) and elastic modulus of unheated and heated samples were determined as important mechanical properties. In addition, the effect of temperature on the physical properties of rhyolite rock (density, color, and absorption) was investigated in conjunction with its microstructural properties. The hardening of the rhyolitic rock samples was observed below 600 °C, at which point the UCS and elastic modulus decreased to 78.0% and 75.9%, respectively, at 800 °C. The results also show that heating does not significantly affect the density and volume of permeable pore space, but a color change can be observed at 400 °C and above. A microscopic analysis shows the change in microstructural properties of rhyolite rock after heating to 600 °C. Furthermore, the SEM observations of heated materials show structural particle displacements and microcracking, leading to apparent surface cracks.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.305

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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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 designBench or experimental
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

Citations15
Published2022
Admission routes1
Has abstractyes

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