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Record W4400235364 · doi:10.11159/iccste24.190

Numerical Study on Flexural Response of Cement Mortars Fortified with Sustainable Graphene Derivative

2024· article· en· W4400235364 on OpenAlexvenueno aff
Mohammad Zuaiter, Tae‐Yeon Kim, Rashid K. Abu Al‐Rub, Fawzi Banat

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
FundersKhalifa University of Science, Technology and Research
KeywordsMortarFlexural strengthGrapheneCementMaterials scienceDerivative (finance)Composite materialBusinessNanotechnology

Abstract

fetched live from OpenAlex

This study aims to predict the flexural response through a 3D non-linear finite element model of plain and modified cement mortar incorporating a sustainable graphene derivative, denoted as D-GSH, synthesized from dune sand and date syrup.The addition of D-GSH in cement mortars was considered by dune sand replacement.Preliminary experimental compressive strength and modulus of elasticity yielded 53 and 45% enhancements upon the addition of 0.3% D-GSH, as a replacement of dune sand.ABAQUS software was employed to simulate a three-point load test on mortar prism specimens, encompassing both tensile cracking and compressive crushing mechanisms.Numerical simulation of three-point bending tests revealed a notable 27% increase in peak load and a 16% larger mid-span deflection upon the addition of 0.3% D-GSH, replaced by dune sand in cement mortars, demonstrating improved resistance and deformability.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference on Civil, Structural and Transportation EngineeringSame topicInnovative concrete reinforcement materialsFrench-language works237,207