Mitigation of alkali–silica reaction in US highway concrete
Bibliographic record
Abstract
This paper provides an overview of the various field trials performed by the US Federal Highway Administration aimed at mitigating the effects of alkali–silica reaction on highway concrete elements, including pavements, bridges and barriers. The different methods used to attempt to reduce the expansion and cracking of affected concrete are described, including the use of sealings and coatings, the application of lithium nitrate and the application of external confinement. The field trials were conducted and monitored between 2005 and 2014, and the overall findings from each trial are briefly summarised. The use of silane products was found to be the most effective means of reducing the expansion and cracking due to alkali–silica reaction, with the most significant improvement seen in highway barriers. Topical and vacuum application of lithium compounds showed little or no benefit in reducing expansion and cracking, mainly due to the lack of lithium penetration. Electrochemical application of lithium nitrate was more effective in driving the lithium into the affected concrete, but had a negligible impact on the expansion and cracking induced by alkali–silica reaction. Owing to the limited time that was available to monitor most of the field trials, future monitoring is essential to delineate the efficacy of the various treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".