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Record W4384008468 · doi:10.1139/cjce-2023-0099

Nano-modified cementitious binders reinforced with basalt fiber/polymer pellets as a stabilizer for weak soils

2023· article· en· W4384008468 on OpenAlexafffundvenueabout
A. Eissa, A. M. Yasien, M. T. Bassuoni, Marolo Alfaro

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of ManitobaConcordia HospitalHatch (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsMaterials scienceCementitiousPelletsLimeStabilizer (aeronautics)Composite materialCalifornia bearing ratioSubgradeNano-Soil stabilizationSoil waterDurabilityCementScanning electron microscopeGeotechnical engineeringCompressive strengthMetallurgyGeologyEngineering

Abstract

fetched live from OpenAlex

Constructing on soft clay entails engineering challenges, such as significant volumetric changes; hence, stabilizing such problematic soil is essential. Since using lime in stabilizing soil is not recommended in some regions (e.g., Manitoba, Canada) due to some environmental concerns, there is a pressing need to explore suitable alternatives. This study investigates the efficiency of stabilizing soft clay using nano-modified cementitious binders (cement, slag, and nano-silica), reinforced with a new class of fibers (basalt fiber pellets). The mechanical and durability properties of the mixtures have been tested in terms of California bearing ratio, unconsolidated–undrained triaxial stresses, and freezing–thawing resistance. Thermogravimetry and scanning electron microscope analyses were performed to interpret the bulk trends. The results showed significant improvement for soft clay specimens stabilized with the nano-modified cementitious binders and reinforced with the pellets. This demonstrates the potential of the proposed reinforced binders for field applications involving the stabilization of soft soils.

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 categoriesMeta-epidemiology (narrow)
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 score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.203
Teacher spread0.191 · 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.

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

Citations3
Published2023
Admission routes4
Has abstractyes

Explore more

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