Nonlinear finite element modeling of bridge piers under the combined effect of corrosion, freeze–thaw cycles, and service load
Bibliographic record
Abstract
Abstract Corrosion of reinforcing steel in reinforced concrete (RC) infrastructure is one of the most concerning durability problems affecting its serviceability and ultimate capacity in North America. The rise of greenhouse emissions in recent decades and the use of de‐icing salts during the winter increase the potential risk of corrosion. Furthermore, global warming could lead to higher freeze–thaw cycles (FTC) frequency in cold regions. The combined effects of corrosion and frost damage tend to affect aging RC infrastructure's structural performance and service life. The present study adopts comprehensive reinforcement corrosion and frost damage models from the literature and proposes a stage‐based damage analysis scenario. Three‐dimensional nonlinear finite element analyses using the commercially available finite element program DIANA are conducted to evaluate the structural performance of RC bridge piers under the synergetic effects of FTCs, corrosion, and service load during their service life. The proposed methodology for each damage mechanism is assessed by comparison with available experimental data from the literature. The synergetic effects cannot be validated because there is no data, but the methodology highlights the deterioration rate at which several mechanisms acting at the same time can affect the structural performance of these members.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".