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Record W4411535677 · doi:10.3390/jcs9070318

Uniaxial Compressive Stress–Strain Model for Bauxite Residue Concrete

2025· article· en· W4411535677 on OpenAlexafffund
Yassine Brahami, M. Fiset, Ali Saeidi, Kadiata Ba, Rama Vara Prasad Chavali

Bibliographic record

VenueJournal of Composites Science · 2025
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsUniversité du Québec à Chicoutimi
FundersCanada Research Chairs
KeywordsBauxiteMaterials scienceCompression (physics)Stress–strain curveCompressive strengthConstitutive equationStress (linguistics)Structural engineeringResidue (chemistry)Strain rateGeotechnical engineeringComposite materialGeologyEngineeringFinite element methodMetallurgyDeformation (meteorology)

Abstract

fetched live from OpenAlex

This paper investigates the full stress–strain response of bauxite residue concrete under uniaxial compression and presents an adapted constitutive model. This study investigates the stress–strain behavior in a series of uniaxial compression experiments and compares to the Popovics, Thorenfeldt et al. and Hoshikuma et al. model predictions. All models accurately predict the pre-peak behavior, showing a very acceptable error rate; however, none of these models adequately predict the post-peak response. A new post-peak stress–strain model is therefore developed in this paper for bauxite residue concrete. The proposed model predictions agreed well with the experimental measurements obtained in this paper for post-peak stress–strain of bauxite residue concrete.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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