MétaCan
Menu
← Back to cohort
Record W4415164178 · doi:10.1139/cjce-2025-0184

Comparative study of mechanical performance and durability of concrete incorporating supplementary cementitious materials

2025· article· en· W4415164178 on OpenAlexafffundvenue
Ahmed G. Mehairi, Saeid Ghasemalizadeh, Rahil Khoshnazar

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPozzolanCementitiousDurabilityFly ashPortland cementSuperplasticizerCompressive strengthCement

Abstract

fetched live from OpenAlex

This study investigates different supplementary cementitious materials (SCMs) as partial substitution for Portland limestone cement in concrete. Five natural pozzolans (two grades of metakaolin, diatomaceous earth, wollastonite, and pumice) and three industrial by-products/wastes (recycled glass powder, Class F fly ash, and reclaimed fly ash) were tested for their chemical reactivity. Those that were classified as reactive were incorporated into concrete at 10, 20, or 40 wt.% of cement. According to results, concretes containing natural pozzolans required significantly higher contents of both superplasticizer and air-entraining admixture, while those with other SCMs required increases only in the air-entraining admixture. The natural pozzolans provided superior compressive strength and durability. The recycled glass powder did not perform as well as the natural pozzolans, however, provided better strength and durability than the fly ashes. The results highlight advantages of performance-based design when using alternative SCMs, especially with the scarcity and increasing price of conventional ones.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Civil Engineering→Same topicConcrete and Cement Materials Research→French-language works237,207→