Simplified Seismic Evaluation of Aged Corrosion Damaged Reinforced Concrete Bridge Columns as Part of Simplified Semi-Quantitative Assessment Framework
Bibliographic record
Abstract
Severe reinforcement corrosion significantly reduces the structural stiffness and load-carrying capacity of Reinforced Concrete (RC) columns. The interactive effects of corrosion-induced damage and repeated traffic load cycles further accelerate bridge columns' load-carrying capacity deterioration. When subjected to seismic excitation, corrosion-affected RC columns could show a dynamic response significantly different from non-affected columns. This paper proposes a Simplified Nonlinear finite element Seismic Analysis approach (SNLSA) based on enhanced inspection of corrosion-damaged RC columns and as a handy tool for evaluating their seismic response, which is a crucial step in a semi-quantitative assessment framework. The SNLSA integrates Nonlinear Sectional Analysis (NLSA), DRAIN-RC computer program for nonlinear time history analysis, and Takeda's hysteretic analysis. The approach provides three options: (i) establish the staged failure mechanism using express analysis simulating quasi-static loading up to failure; (ii) use a more comprehensive analysis simulating cyclic loading developing the hysteretic relationships; and (iii) conduct a nonlinear full time-history analysis. The SNLSA can estimate the significant contraction of the column interaction capacity when subjected to severe corrosion damage for all load-over-capacity ratios. The SNLSA quantitatively predicts the change in the seismic performance of corrosion-affected versus as-built bridge columns. The approach could also be used to select the appropriate design option for bridge columns in seismic-critical zones.
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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) | 0.000 | 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".