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Record W4318478510 · doi:10.1680/jgein.22.00310

Compressive and shear response of fibre-reinforced backfill: impact of field temperature

2023· article· en· W4318478510 on OpenAlexaff
Xichun Tian, Mamadou Fall

Bibliographic record

VenueGeosynthetics International · 2023
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIsothermal processCuring (chemistry)Materials scienceComposite materialCompressive strengthMicrostructureThermodynamics

Abstract

fetched live from OpenAlex

Fibre-reinforced cemented paste backfill (F-CPB; a mixture of tailings, fibre, cement, and water) is exposed to non-isothermal curing environments in the field. However, no investigations have been conducted on the impact of non-isothermal curing environments on F-CPB compressive strength, shear characteristics and stress–strain behaviour, although these are important design parameters for F-CPB structures. Therefore, a series of mechanical and microstructural tests were performed on samples with and without fibres exposed to isothermal and non-isothermal conditions. It was found that the non-isothermal curing condition substantially enhances the F-CPB compressive strength and strength gain ratio. The shear strength, cohesion and internal friction angle of the F-CPB samples cured under non-isothermal conditions were higher than those cured under isothermal conditions. These impacts of non-isothermal curing on the compressive and shear characteristics of F-CPBs are related to the temperature-induced change in the microstructure of these samples. The F-CPBs cured under non-isothermal conditions had more hydration products than those cured under isothermal conditions. Moreover, mercury instrusion porosimetry (MIP) tests showed that the F-CPBs subjected to non-isothermal conditions had a finer pore structure than those exposed to isothermal conditions, which contributed to an enhancement of the mechanical characteristics of the F-CPBs subjected to non-isothermal curing.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.244

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 head0.009
GPT teacher head0.242
Teacher spread0.232 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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