Durability of Road and Bridge Concrete and Spray-Coating Waterproof Material
Bibliographic record
Abstract
Different from traditional waterproof methods for road and bridge concrete, the spray-coating waterproof material can effectively prevent rainwater and other corrosive liquids from entering the concrete structure, and it has a few functional advantages such as anti-carbonization, resistance to sulfate attack, and high elongation. Existing studies have discussed the impact of the microstructure of waterproof material on the structure of waterproof coating, the optimization of the mix ratios of waterproof material, and the influencing factors of the waterproof performance of waterproof material, etc., however, few researches have concerned about the mechanical properties of concrete after coated with the spray-coating waterproof material. Therefore, to fill in this research gap, this paper researched the durability of road and bridge concrete and the spray-coating waterproof material. At first, it analyzed the bridge deck water pressure and the internal force of the drain pipe structure under multiple drainage methods, and calculated the water pressure at the gutter inlet, the stress, and the permeability coefficient of the waterproof coating. Then, this paper elaborated on the experimental methods for measuring the durability of the road and bridge before and after coated with the spray-coating waterproof material, and introduced the methods for testing the waterproof material and the concrete. At last, this paper gave the corresponding experimental results, the analysis, and the conclusion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".