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Record W4414367487 · doi:10.1139/cjce-2024-0417

Evaluating performance of large industrial concrete slabs-on-grade: a focus on fiber reinforced concrete and shrinkage compensating concrete

2025· article· en· W4414367487 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete Properties and Behavior
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsShrinkageCrackingReinforcementReinforced concreteFiber-reinforced concreteUltimate tensile strength

Abstract

fetched live from OpenAlex

One of the structures that is highly susceptible to cracking is large industrial slabs. This study investigates the response of large industrial concrete slabs under the influence of temperature and shrinkage loads. As a case study, the performance of fiber reinforced concrete (FRC), shrinkage compensating concrete, and normal concrete in 8 in. thick slabs over a 90-day period is evaluated. Utilizing a detailed numerical model in ABAQUS, the study examines crack width and patterns, revealing that FRC slabs show enhanced shrinkage and cracking resistance. The impact of various reinforcement configurations, including top, bottom, and no reinforcement, on cracking behavior is also explored. Results indicate that bottom reinforcement in slabs has a better performance than top reinforcement in mitigating cracking. The study highlights that FRC significantly curtails crack lengths and boosts tensile strength, offering a more effective solution for crack control in industrial slabs subjected to temperature and shrinkage stresses.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.240
Teacher spread0.214 · 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