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Record W3110831435 · doi:10.1016/j.jrmge.2020.11.002

Chemically induced changes in the geotechnical response of cementing paste backfill in shaking table test

2020· article· en· W3110831435 on OpenAlexafffund
Imad Alainachi, Mamadou Fall

Bibliographic record

VenueJournal of Rock Mechanics and Geotechnical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsGeotechnical engineeringEarthquake shaking tableGeologyTest (biology)

Abstract

fetched live from OpenAlex

Cemented paste backfill (CPB) is extensively used for underground mine support and/or tailings management. However, CPB behavior under cyclic loadings might be affected by the chemistry of its pore-water, which often contains sulphate ions. Till today, no studies have addressed the effect of sulphate on the response of CPB to cyclic loadings by using shaking table technique. This study presents new findings of assessing the effect of the sulphate in the pore water of CPB on its geotechnical response to cyclic loading by using shaking table. CPB mixtures were prepared (with and without sulphate), poured into a flexible laminar shear box, cured to 4 h, and then exposed to cyclic loading using one-dimensional (1D) shaking table. Several parameters (e.g. pore water pressure , settlement, lateral deformation , acceleration, electrical conductivity , effective stress, and liquefaction susceptibility) were monitored or determined before, during, and after shaking. Obtained results indicate that the sulphate-bearing CPB cured to 4 h can be prone to liquefaction under the studied conditions. However, sulphate-free CPB samples are resistant to liquefaction. These results are expected to contribute to a better understanding of the effect of water chemistry on the cyclic behavior of CPB, consequently enhancing the cost-effective design of CPB structures.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.374
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.195
Teacher spread0.179 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Rock Mechanics and Geotechnical EngineeringSame topicTailings Management and PropertiesFrench-language works237,207