Seismic Drift Estimates of Corroded Piers: A Multihazard Approach Utilizing 3D‐IDA Analysis With Time Stamps Considering Climate Change Effects
Bibliographic record
Abstract
ABSTRACT The compounding effect of seismicity, exposure to corrosion, and climate change in regions such as North America pose significant challenges for bridge design engineers, as the multihazards impact on the seismic performance of bridge structural systems remains underexplored. While drift ratio is the most widely used parameter in probabilistic seismic demand assessment, existing models predominantly concentrate on seismic intensity levels, overlooking the increase in demand and the reduced deformation capacity, both being affected by corrosion‐induced damage and climate change. Therefore, developing an evaluation framework for the multihazard seismic vulnerability of deficient bridges is an emerging priority in the field. To address this need, a methodology is proposed here that uses data collected from field inspections to quantify the accumulation of historic corrosion damage and forecast future corrosion propagation. Climate change scenarios derived from future climate forecast models are used to project the temperature and relative humidity changes up to the year 2100; the rate of reinforcement corrosion is quantified based on these scenarios. Utilizing incremental dynamic analysis (IDA) across a projected timeline, expressions are derived for the time evolution of drift demand in existing reinforced concrete circular piers over the lifetime of the bridge. By using these results, drift demand expressions are derived for different climate change scenarios. The influence of design parameters (e.g., concrete cover, chloride diffusion coefficient, and aging factor) on the drift demands is evaluated using Monte Carlo simulation. The proposed expressions serve as a benchmark for bridge engineers to study the seismic performance of bridge structures in multihazard environments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".