Effects of Mixed Corrosion, Freeze-Thaw Cycles, and Persistent Loads on Behavior of Reinforced Concrete Beams
Bibliographic record
Abstract
The effects of mixed corrosion and freeze-thaw cycles on the mechanical properties of concrete prism specimens and the effects of mixed corrosion, freeze-thaw cycles, and persistent loads on the structural behavior of reinforced concrete beams were experimentally studied. A mixed solution of NaCl and Na2SO4 was used as a corrosive medium. Results show that under alternating actions of freeze-thaw and mixed corrosive agents, increasing the number of freeze-thaw cycles decreases the compressive strength and the elastic modulus of concrete and increases the compressive strain corresponding to the maximum compressive stress. The degradation of concrete material properties accelerates with the increase of water-cement ratio. For reinforced concrete beams, a 4% reduction in the loading capacity is found when these are subjected to freeze-thaw cycles and mixed corrosion only. However, if these actions are coupled with persistent loading, as expected during the service life of reinforced concrete structures in cold regions, a more rapid drop in the strength and deformation capacity of the beams is identified. The degradation is enhanced by a larger persistent-loading ratio. The significance of an accurate simulation of service conditions in the durability study of reinforced concrete structures in cold regions is highlighted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".