Use of wet cellulose to cure shotcrete repairs on bridge soffits. Part 1: Field trial and observations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".