Use of wet cellulose to cure shotcrete repairs on bridge soffits. Part 2: Laboratory testing and analysis
Bibliographic record
Abstract
This paper reports results of an experimental program conducted to investigate the quality of shotcrete cured by wet, sprayed-on cellulose fibers. The program also investigated the quality of shotcrete containing accelerator. Both field and laboratory samples were prepared and cured using air-curing, curing compound, misting and curing compound, or cellulose. At the end of the curing period, cores were collected from the test panels and tested. The results showed that the effects of curing are noticeable mainly at the top 15 mm of the shotcrete surface. Within this part of the shotcrete, cellulose-cured panels showed an enhanced pore structure compared to samples cured using traditional curing methods, as reflected by mercury intrusion porosimetry and sorptivity test results. The quality of the surface of cellulose-cured shotcrete was significantly enhanced when the additives added to commercially available cellulose to improve its fire resistance were eliminated. Adhesive added to improve bonding of cellulose fibers to the shotcrete surface did not have a negative effect on the surface of the shotcrete. Laboratory-prepared samples showed evidence that cellulose curing reduces shrinkage cracking. The use of accelerator was found to adversely affect the strength and durability of the shotcrete.Key words: bridge repair, shotcrete, accelerator, salt scaling, curing, cellulose, permeability, sorptivity, pore structure, degree of hydration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".