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Record W4280579070 · doi:10.1139/cgj-2021-0460

Improved cone penetration test predictions of the state parameter of loose mine tailings

2022· article· en· W4280579070 on OpenAlexvenueno aff
Juan Ayala, Andy Fourie, David Reid

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersUniversity of South AustraliaUniversity of WollongongUniversity of New South WalesFreeport-McMoRan Foundation
KeywordsTailingsGeotechnical engineeringCalibrationSoil waterPenetration testPenetration (warfare)Tailings damGeologyEnvironmental scienceEngineeringMaterials scienceSoil scienceMathematics

Abstract

fetched live from OpenAlex

The most widely used in situ testing instrument for tailings storage facility (TSF) is the cone penetration test (CPT), which uses calibration chamber (CC) test data as the preferred method to correlate the soil's state with the CPT acquired data. Many, if not most, mine tailings are different from the soils historically used in CC studies (mainly sands). Moreover, these tests were conducted at denser states than typically found in TSFs. Additionally, CC tests cannot practically be performed for every new soil-specific project. For these reasons, practitioners usually perform numerical simulations to approximate these correlations. One of the most popular techniques for this is the spherical cavity expansion approach. This study compares novel small CC testing of two different silty mine tailings, and their respective spherical cavity expansion simulations. Since these simulations use the NorSand constitutive model, this study also presents the calibration process that includes the triaxial laboratory testing to calibrate the NorSand properties, and the corresponding iterative process to infer the additional plastic and elastic parameters of the model. Finally, an adjustment equation is introduced for the contractive state of these silty materials.

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 score0.042
Threshold uncertainty score0.421

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 integrity0.0000.001
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 head0.006
GPT teacher head0.174
Teacher spread0.168 · 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

Citations26
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207