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Record W3194772259 · doi:10.1139/cgj-2021-0087

Exploring the mechanical response of low-carbon soil improvement mixtures

2021· article· en· W3194772259 on OpenAlexvenueno aff
Alessandro Fraccica, Giovanni Spagnoli, Enrique Romero, Marcos Arroyo, Rodrigo Gómez

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsMetakaolinSiltGeotechnical engineeringSoil waterMaterials scienceGeopolymerPortland cementColloidal silicaPermeability (electromagnetism)Soil stabilizationCompressive strengthGeologyComposite materialCementSoil scienceChemistry

Abstract

fetched live from OpenAlex

As society moves towards decarbonisation, it is important to assess the hydromechanical behaviour of binders that could offer a low-carbon alternative to Portland cement in ground improvement technologies. This work considers two such alternatives: one still largely unexplored (metakaolin-based geopolymers) and a better known one (colloidal silica). Results from unconfined compressive strength, permeability tests, undrained monotonic and cyclic triaxial tests on granular soils (sand and silty sand) treated with those two binders are presented and discussed, emphasizing similarities and differences with the response of similar soils treated with other conventional and unconventional binders. Effects of silt content, curing conditions and soil/binder ratios are examined. Both colloidal silica and metakaolin-based geopolymer significantly improve the mechanical properties of the treated soils, although the geopolymer results in a stronger and stiffer material. Both treatments reduce much the permeability of the treated soil, but the reduction achieved with colloidal silica is larger.

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)0.000
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 score0.009
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.229
Teacher spread0.199 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

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