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Record W2913609617 · doi:10.1139/cjce-2018-0075

Mitigation of plastic shrinkage in fly ash concrete using basalt fibres

2019· article· en· W2913609617 on OpenAlexvenueno aff
T. Hemalatha, Gomasa Ramesh

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsShrinkageDurabilityCrackingFly ashMaterials scienceComposite materialCementAlkalinity

Abstract

fetched live from OpenAlex

Many durability related problems in concrete structures are caused due to early-age cracking. Though early-age cracking is not detrimental to the structure, this will open up the way for the long-term durability issues, hence, needs to be mitigated. Cracking of such types in concrete is commonly prevented by adding fibres. Present work aims at studying the potential use of basalt fibres in controlling the shrinkage cracks at early age, otherwise this fibre is considered not suitable for use in concrete as it would degrade under alkaline environment. Further, to sustain the long-term durability, an attempt has been made to reduce the alkalinity of the concrete by replacing the cement with fly ash up to 50%. Concrete specimens made with two water to binder ratios (w/b), six replacement levels of fly ash, and one type of fibre are used for this study. Crack width and crack area generally used for evaluating the extent of shrinkage cracks are measured and analyzed. As the shrinkage cracks are very thin to measure manually, image analysis technique has been employed to measure the crack widths. Results indicate that to a greater extent cracks developed during early age has been effectively mitigated with the incorporation of fly ash. Further, it is found that basalt fibres are effective in arresting the shrinkage cracks. Furthermore, it is concluded that manual measurement will under or overestimate the crack width, hence, image analysis technique can be successfully used for measuring the crack widths.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.187
Teacher spread0.180 · 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 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

Citations14
Published2019
Admission routes1
Has abstractyes

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