New Life Expectancy. Self-curing Concrete from Canada and Long-lived Bridge Decks
Bibliographic record
Abstract
This article describes a more durable concrete that will increase the average lifespan of bridge decks by more than 20 years compared to typical high-strength concrete, and by more than 40 years compared to normal-strength concrete. This high-performance concrete has been specially formulated to minimize shrinkage, which is typical of high-strength concrete, while maintaining its excellent mechanical properties. It also greatly reduces cracking, which diminishes the penetration of aggressive agents into the concrete, such as chlorides from the de-icing salts used on roads. As a result, it takes considerably more time for the chlorides to reach the steel reinforcement, initiate corrosion and induce further damage to the structure. The key difference is in the sand: lightweight, porous, shale-fine aggregate, which replaces about a quarter of the normal sand used to make concrete. This porous sand can hold up to 20 percent of its own weight of water, which serves to cure the concrete uniformly from the inside, thus preventing self-desiccation. With a unit cost only 5 percent higher than that of a standard high-strength concrete, Cusson expects concrete bridge decks made with this new concrete to last longer, saving taxpayers money in annual bridge maintenance, recurring repairs and associated traffic disruption, and replacement.
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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) | 0.005 | 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".