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Record W4401162276 · doi:10.1139/cgj-2024-0077

Accelerated carbonation of non-plastic soils mixed with hydrated lime: index properties influencing binder formation rate and mechanical improvement

2024· article· en· W4401162276 on OpenAlexvenueno aff
Aaron P. Gallant, SK Belal Hossen, Warda Ashraf

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
FundersU.S. Department of Transportation
KeywordsCarbonationLimeGeotechnical engineeringSoil waterMaterials scienceComposite materialIndex (typography)GeologyMetallurgySoil science

Abstract

fetched live from OpenAlex

Chemical stabilization—the mixing of additives like cement, lime, or fly-ash with soil to improve its mechanical properties—conventionally relies on hydration reactions to generate a binder. Accelerated soil carbonation is a nascent alternative method, whereby carbon dioxide is intentionally introduced in soil mixed with alkali additives to generate a carbonate binder and sequester carbon dioxide. Non-plastic sand and silt specimens mixed with hydrated lime were carbonated for varying amounts of time at different water contents and densities to evaluate the index properties influencing the rate of carbonation and degree of mechanical improvement. It was demonstrated that volumetric air and water contents primarily govern the rate of binder formation and that mechanical properties are influenced by the carbonate binder content and density. Under optimum conditions, soil specimens could be fully carbonated within 3–24 h and unconfined compressive strengths as great as 3–4 MPa were achieved. The degree of strength improvement is comparable to cement-stabilized materials and has a similar dependence on soil type, density, and binder content. If techniques are developed that enable carbonation at scale, the sequestration of carbon dioxide would offset emissions associated with production of chemical additives used for chemical stabilization.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.621

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 head0.010
GPT teacher head0.188
Teacher spread0.178 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGrouting, Rheology, and Soil MechanicsFrench-language works237,207