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Record W4225158124 · doi:10.11159/icsect22.116

Transport Properties of Pozzolanic Concrete Based on the South African Durability Indexes

2022· article· en· W4225158124 on OpenAlexvenueno aff
Victor S. Gilayeneh, Sunday Nwaubani

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsDurabilityPozzolanPozzolanic activityMaterials scienceComputer scienceComposite materialCementPortland cement

Abstract

fetched live from OpenAlex

Engineers and designers often incorporate pozzolans in concrete to enhance specific properties, most notably, its resistance to ionic penetration that directly depends on the cover concrete's transport properties, which are a function of the microstructure. This paper describes the influence of pozzolans on concrete microstructure and durability-related transport properties measured according to the South African durability indexes. The resistance of pozzolanic concrete to oxygen permeation, water absorption and chloride diffusion was evaluated. The pozzolanic materials considered were fly ash, blast-furnace slag, silica fume and metakaolin. These materials were used as partial replacement for the Portland cement in binary mixes. At the ages tested, the metakaolin mix displayed excellent performance in strength development, microstructure enhancement and durability. The results also show that the mixture incorporating metakaolin exhibited the highest resistance to oxygen permeation, chloride conduction, and the lowest porosity, followed by the blastfurnace slag mix. The silica fume mix displayed the highest resistance to water absorption, while the fly ash mix exhibited the most moderate resistance to water absorption and chloride conduction.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.581

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 head0.008
GPT teacher head0.166
Teacher spread0.158 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207