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Record W2109527949 · doi:10.1139/l06-023

Use of wet cellulose to cure shotcrete repairs on bridge soffits. Part 1: Field trial and observations

2006· article· en· W2109527949 on OpenAlexvenueno aff
Medhat Shehata, Mike Navarra, Tom Klement, Mohamed Lachemi, H C Schell

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsShotcreteCuring (chemistry)CelluloseMaterials scienceComposite materialCrackingChemistryGeotechnical engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

This paper presents the results obtained from a research project that focused on investigating the feasibility of using cellulose fibers to cure bridge soffit repairs. The use of cellulose as a curing method involves spraying wet cellulose on the freshly applied shotcrete. By adhering to the shotcrete, the wet cellulose maintains the relative humidity within the shotcrete above the level required to sustain hydration of the cementing materials. Twelve 1000 mm × 1000 mm × 130 mm panels were prepared using two types of shotcrete materials and cured using either air curing, curing compound, misting and curing compound, or cellulose. The results showed that the cellulose could be applied to shotcrete in an overhead position and remained adhered to the shotcrete for 28 days. At the end of the curing period, the cellulose was easily removed from the shotcrete surface by means of a hand shovel. Cellulose-cured panels showed the least evidence of surface cracking. Also, the use of cellulose did not have any negative effects on the temperature of the shotcrete.Key words: bridge repair, shotcrete, silica fume, accelerator, polypropylene fibers, curing, cellulose, heat of hydration, adhesion.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.723

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 head0.035
GPT teacher head0.204
Teacher spread0.170 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

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