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Record W4391721320 · doi:10.1007/s00603-023-03751-z

Assessment of Direct Tensile Strength Tests in Rock Through a Multi-laboratory Benchmark Experiment

2024· article· en· W4391721320 on OpenAlexaffabout
Ignacio Pérez–Rey, Mauro Muñiz-Menéndez, Thomas Frühwirt, Heinz Konietzky, Lars Jacobsson, Matthew A. Perras, Kamelia Atefi‐Monfared, Diego Mas Ivars, Abel Sánchez Juncal, Leandro R. Alejano

Bibliographic record

VenueRock Mechanics and Rock Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsYork University
FundersXunta de GaliciaUniversidade de Vigo
KeywordsRepeatabilityReproducibilityRock mechanicsUltimate tensile strengthGeotechnical engineeringUniaxial tensionTensile testingStandard deviationBenchmark (surveying)Tension (geology)European standardPoisson's ratioGeologyMathematicsMaterials sciencePoisson distributionEngineeringStatisticsComposite materialGeodesy

Abstract

fetched live from OpenAlex

Abstract This study aims to experimentally assess repeatability and reproducibility of direct tensile strength (DTS) tests with deformability measurements on two types of rocks: Blanco Mera granite (Spain) and Cotta sandstone (Germany). The tests were conducted in four rock mechanics laboratories located in different countries (Canada, Germany, Spain and Sweden). A total of 51 tests were performed on cylindrical specimens of the two rocks, using different test equipment and measuring devices. Mean and standard deviation DTS values were determined in the four laboratories for the granite (5.70 ± 0.32, 6.06 ± 0.11, 3.84 ± 0.50 and 6.76 ± 0.10 MPa) and for the sandstone (1.88 ± 0.07, 1.96 ± 0.06, 1.15 ± 0.32 and 1.74 ± 0.19 MPa), together with Young’s moduli and Poisson’s ratios in tension, being statistically analysed to evaluate the variability and compare the main results obtained from the participating laboratories. The findings indicate that the DTS test with deformability measurements on cylindrical rock specimens is operationally feasible. However, certain shortcomings have been identified during the course of the experiments with the existing methodologies, such as the one suggested by the ISRM for DTS tests. The results have also shown to be sensitive to appropriate test and strain measurement configurations. The objective of this study was to shed light on these issues and provide new insights for potential future improvements of the existing testing methods.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2024
Admission routes2
Has abstractyes

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