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Record W4220732449 · doi:10.1139/cjce-2021-0404

Superabsorbent cellulose fibers for reducing shrinkage and microcracking in concrete

2022· article· en· W4220732449 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Properties and Behavior
Canadian institutionsLakehead University
FundersLakehead University
KeywordsShrinkageCrackingDurabilityMaterials scienceComposite materialSuperabsorbent polymer

Abstract

fetched live from OpenAlex

One of the major durability issues in concrete structures is the cracking resulting from shrinkage. Drying shrinkage contributes a major portion of shrinkage strain in conventional concrete. Controlling the drying shrinkage of concrete can lower the total shrinkage strain and subsequently reduce the extent of cracking and enhance the durability of concrete structures. The objective of this study was to develop a pulp fiber-based concrete to reduce the drying shrinkage and microcracks in concrete. Using superabsorbent cellulose fibers (SCFs) in the concrete mix, this study intended to develop a concrete mix with acceptable properties with reduced shrinkage strain and cracking. Using scanning electron microscope images, crack growth in different concrete specimens with and without SCFs was observed. Free shrinkage tests were conducted to investigate the efficiency of SCFs in controlling concrete shrinkage. The test results indicated significantly reduced crack widths and a lower shrinkage strain in concrete containing SCFs.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.601

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 head0.009
GPT teacher head0.174
Teacher spread0.166 · 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