Concrete Infrastructure: Recent Advancements and Needs with a Focus on North America
Bibliographic record
Abstract
This letter provides an overview of the continent’s diversity in geography and climatic exposure and the impact of chlorides on reinforced concrete structures in North America. Several research needs are identified, including those that arise as specifications begin to change from prescriptive to performance-based approaches. Further, the widespread changes in material compositions or chloride exposures, require important changes to specifications, design practices, or maintenance procedures. Related to reducing carbon emissions, there is a need to reduce clinker content in concrete mixtures, increase the use of novel cementitious and supplementary cementitious materials (SCM), and to understand the durability of such concretes. The following research efforts from a North American context are warranted: (i) investigating the long-term durability of novel cement and SCM systems, including non-Portland cement-based materials and those made with CO2 mineralization used to meet carbon emission targets; (ii) understanding climate change impacts of temperature and sea levels, including flood impact, on chloride exposure and chloride-induced corrosion; (iii) developing rapid and reliable tests to estimate durability in practice, particularly for scaling, freeze-thaw, salt damage, and chloride-induced corrosion; and (v) developing better understanding of the short and long-term implications of changes in constituent materials and exposure.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".