Corrosion-Inhibiting Systems for Durable Concrete Bridges. I: Five-Year Field Performance Evaluation
Bibliographic record
Abstract
The performance of nine commercially available corrosion-inhibiting systems for use in reinforced concrete structures exposed to corrosive environments was assessed in the field on bridge barrier walls and in accelerated electrochemical cells in the laboratory. The corrosion-inhibiting systems included concrete admixtures, reinforcing steel coatings, and/or concrete surface coatings/sealers. The results of this study are presented in two companion papers, in which the field evaluation and laboratory investigation are reported. The field evaluation consisted of annual corrosion surveys of half-cell potential and corrosion rate, as well as remote monitoring with embedded instrumentation for the measurement of the environmental conditions. After five years of investigation, the system containing an inorganic-based admixture performed better than others in reducing the risk of reinforcement corrosion in the barrier wall. It was also found that the system using epoxy-coated reinforcement showed good early performance during the first year, but after, the risk of corrosion increased relatively faster than in other systems, possibly due to localized pitting corrosion developing in small defects or pores of the epoxy coating.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".