Combined Effects of Freeze-Thaw and Corrosion on Performance of RC Structures: State-of-the-Art Review
Bibliographic record
Abstract
Freezing and thawing cycles (FTC) on RC columns are a significant problem for vulnerable infrastructure exposed to extreme climate conditions. This problem is exacerbated by the presence of deicing agents that lead to reinforcement corrosion and overall concrete deterioration. Current research has mainly focused on studying the mechanical properties of concrete when exposed to cyclic conditions of freezing and thawing. Few studies have analyzed FTC’s influence or the dual action of FTC and steel corrosion on the structural performance of RC. This paper surveys available literature on the synergistic effects of one or multiple environmental exposures on RC columns and methodologies for inducing frost damage according to current standards. The literature survey is organized as follows: (1) frost damage mechanism; (2) test methods to evaluate frost damage; (3) effect of FTC on concrete mechanical properties; (4) effect of FTC on the structural performance of RC columns; and (5) effect of dual action of FTC and steel corrosion on RC columns. Finally, this paper draws a series of conclusions and recommendations for future work.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".