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Record W3095273812 · doi:10.1139/cjce-2020-0129

Simultaneous effects of nanosilica and basalt fiber on mechanical properties and durability of cementitious mortar: an experimental study

2020· article· en· W3095273812 on OpenAlexvenueno aff
Mehrdad Razzaghian Ghadikolaee, Mehdi Mirzaei, Asghar Habibnejad Korayem

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
FundersIran University of Science and Technology
KeywordsBasalt fiberDurabilityMaterials scienceSorptivityCompressive strengthComposite materialUltimate tensile strengthMortarFlexural strengthAbsorption of waterFiberCementitiousBasaltCementGeology

Abstract

fetched live from OpenAlex

This study investigated the single and hybrid effects of nanosilica and basalt fiber on mechanical properties and durability of mortar. Results showed that basalt fiber could remarkably increase the indirect tensile strength, whereas the compressive strength and durability properties were not significantly improved by basalt fiber. However, incorporation of nanosilica in the mortar containing basalt fibers could acceptably compensate for this weakness of basalt fiber-reinforced mortar (BF) and remarkably improve not only the compressive strength and durability but also the indirect tensile strength compared to BF samples. According to the best results, samples containing 1% of nanosilica and 0.05% of basalt fiber improved the compressive strength, sorptivity, and water absorption by 37%, 48%, and 32%, respectively. Moreover, incorporation of 1% of nanosilica and 0.125% of basalt fiber increased the flexural strength, splitting tensile strength, and specific electrical resistivity by 29%, 27%, and 35%, respectively, compared to the control samples.

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 score0.038
Threshold uncertainty score0.596

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 head0.014
GPT teacher head0.198
Teacher spread0.184 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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