Parametric study of medium span bridges retrofitted with reinforced concrete jacketing
Bibliographic record
Abstract
Most reinforced concrete (RC) bridges in many countries are medium-span length structures built in the last decades and designed for very low seismic forces. The evolution of seismic codes and the average age of the bridges require the evaluation of their seismic vulnerability. This study assesses the expected capacity, demand and damage of seismically deficient medium-length highway bridges, supported in frame-type piers using dynamic nonlinear methodologies. A parametric study of reinforced concrete retrofitted bridges with RC jacketing was conducted. The non-retrofitted structures are 30 m span simple supported bridges with pier heights in the range of 5–25 m. The main emphasis of the study is the assessment of the jacket parameters’ contribution to the seismic vulnerability of bridges. Particularly, it is quantified how jacket thickness and reinforcement ratio affect the probability of reaching a particular damage limit state. The retrofitted scheme includes three jacket thicknesses and three different longitudinal steel ratios. The results evaluate bridge demands and fragility curves to quantify the influence of RC jacketing on the seismic response of structures and allow to select the best jacket parameters that improve the expected seismic behavior of the bridge models. Additionally, the influence of model hysteresis degradation on the expected damage of retrofitted bridges was also determined.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".