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Record W4414187177 · doi:10.1016/j.jobe.2025.114101

Low-carbon gypsum-modified concrete with 80 % cement reduction and strength restoration by confinement

2025· article· en· W4414187177 on OpenAlexafffund
Ali Alinejad, Pedram Sadeghian, Amir Fam

Bibliographic record

VenueJournal of Building Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsQueen's UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCementCompressive strengthGypsumFly ashCore (optical fiber)Strength reduction

Abstract

fetched live from OpenAlex

Recycled gypsum powder from waste drywalls, combined with fly ash, provides a sustainable alternative for partial cement replacement in low-carbon concrete. This study aims an ambitious 80 % reduction in cement content, targeting both the mitigation of environmental impacts from cement production and the diversion of drywall waste through the use of recycled gypsum. The study established the optimal proportions of recycled gypsum and fly ash. Recycled gypsum was incorporated at 20 % and 40 %, with fly ash replacing 30 % and 50 % of binder weight, resulting in a significantly reduced cement content of only 21 % of the binder. The concrete achieved compressive strengths of 9.3 MPa and 15.6 MPa after 28 and 90 days, respectively. The second objective of the study is to demonstrate that the reduced strength of such ultra-low-cement concrete can be effectively restored through confinement in fiber-reinforced polymer (FRP) tubes, enabling its use in structural applications. Structural performance was evaluated using six CFFTs with ±55° glass-FRP (GFRP) tubes, with four subjected to monotonic loading and two to cyclic loading. The synergistic effect of confinement enhanced the compressive strength of the low-carbon concrete core by a factor of 2.1–4.8 in monotonic tests and 2.1–2.5 in cyclic load tests. These findings indicate significant potential for low-carbon, gypsum-modified concrete in eco-friendly, large-scale infrastructure applications.

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 score0.113
Threshold uncertainty score0.515

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 head0.007
GPT teacher head0.225
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

Citations1
Published2025
Admission routes2
Has abstractyes

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