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Record W3173559468 · doi:10.11159/icsect21.lx.109

A Comparative Study of Al2O3, ZnO and Bentonite Effect on Structural Grade Mortar

2021· article· en· W3173559468 on OpenAlexvenueno aff
Suvash Chandra Paul, Adewumi John Babafemi, Md Jihad Miah

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsBentoniteMortarMaterials scienceComposite materialGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

In recent years, nanoparticles have gained much attention in producing cementitious materials to modify both fresh and hardened properties, including their long-term durability characteristics. Due to its higher fineness and surface area, superior pozzolanic properties, nanoparticles have shown more significant improvement in cementitious materials' microstructure. Additionally, some micro filler materials also can improve the properties of hardened cementitious materials by filling up the micropores. Within this context, the focus of this paper is to investigate the optimum dosages of nano-Al2O3, nano-ZnO and bentonite in the production of medium strength (about 25-35 MPa) structural grade mortar. For the characterisation of materials properties, a compressive strength test is performed. The experimental results showed that up to a certain dosage of both nano-Al2O3 and bentonite, compressive strength is increased. After that, increasing dosages lead to a decrease in strength. However, strength can be significantly reduced when nano-ZnO is used in the mix.

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 score0.138
Threshold uncertainty score0.867

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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicConcrete and Cement Materials ResearchFrench-language works237,207