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Record W4412673579 · doi:10.1016/j.cscm.2025.e05081

Hydration behavior, microstructural characteristics, and kinetic analysis of cemented tailings backfill under temperature effect

2025· article· en· W4412673579 on OpenAlexaff
Chao Zhang, Weidong Song, Jinping Guo, Abbas Taheri, Yuan Wang, Xiaolin Wang

Bibliographic record

VenueCase Studies in Construction Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsQueen's University
FundersKey Research and Development Projects of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsTailingsMaterials scienceGeotechnical engineeringKinetic energyMetallurgyComposite materialGeology

Abstract

fetched live from OpenAlex

The hydration behavior of cemented tailings backfill (CTB) is critical to understanding its mechanical performance and microstructural evolution. This study proposes a modified hydration kinetics model that accounts for the dilution, dissolution, and nucleation effects of tailings, with variable growth rates. The model was validated through isothermal calorimetry and closely matched experimental data. Results show that elevated curing temperatures accelerate hydration reactions, with both the maximum growth rate and critical nucleus length exhibiting S -shaped trends. The degree of hydration peaks at 40 ℃, beyond which further increases in temperature reduce hydration efficiency due to early surface densification and hindered ion transport. Kinetic parameters were found to exponentially shorten hydration stage durations, while cumulative heat release increases and then declines with temperature. These findings improve the understanding of temperature-dependent hydration mechanisms and provide theoretical support for optimizing CTB mix design and curing strategies in deep mining environments, where thermal conditions critically affect early strength development and backfilling efficiency.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.568

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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