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Record W4402905623 · doi:10.1139/cgj-2023-0713

Ultrasonic interrogation of a clayey tailings during sedimentation–consolidation

2024· article· en· W4402905623 on OpenAlexafffundvenue
Hirlatu Peruga, Misagh Khanlarian, Giovanni Cascante, Paul Simms

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsUniversity of WaterlooCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada's Oil Sands Innovation Alliance
KeywordsConsolidation (business)Geotechnical engineeringTailingsGeologyTailings damClay soilMaterials scienceSoil waterSoil science

Abstract

fetched live from OpenAlex

Ultrasonic methods are used for non-destructive detection of changes of low-strain properties in concrete, soils, and other materials. This paper presents the use of ultrasonics to detect density and structure changes in a soft clayey tailings. Such tailings undergo sedimentation and consolidation following their deposition, which may occur simultaneously with flocculation and other fabric-altering processes, resulting in time-dependent consolidation characteristics. Here we attempt to measure structural changes in the clayey tailings using ultrasonics, which presents several challenges due to the material’s relative softness. Ultrasonics testing was attempted using a special 3D printed column with height and diameter of 540 and 110 mm, respectively. Specially fabricated sample holders were designed to repeatedly ensure proper connections of compression (P-wave) and shear (S-wave) wave transducers. The arrival time corresponding to the largest P-wave amplitude showed a strong correlation with density of the tailings, while a specific frequency bandwidth of the P-wave spectra indicates an increase in wave amplitude (signal energy) occurring subsequent to most of the volume change. Ultrasonic measurements appear to be a promising technique to monitor density and stiffness in soft clays, tailings, and similar soft sediments.

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 score0.429
Threshold uncertainty score0.627

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 head0.198
Teacher spread0.193 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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