Comparative Performance Evaluation of Retrofit Alternatives for Upgrading Simply Supported Bridges Using 3D Fiber-Based Analysis
Bibliographic record
Abstract
This study aims to select an effective mitigation approach from different alternatives to upgrade substandard RC bridges to meet the seismic performance objectives of current design standards. The performance assessment results for an existing benchmark bridge confirmed that the bent curvature ductility and bearing displacement control the seismic response. Thus, five contemporary retrofit solutions were investigated, including adding different supplementary lateral force-resisting systems (SLFRSs), replacing old bearings with those equipped with shape memory alloy (SMA), and combinations of these retrofit options. Fourteen earthquake records representing long- and short-period seismic events and the seismo-tectonic characteristics of a moderate seismic region were progressively scaled and applied separately in the two orthogonal directions of detailed simulation models representing the retrofitted benchmark bridge. This study provided insights into the impact of combining contemporary seismic risk mitigation techniques on improving the seismic performance of substandard bridges and presented a range of fragility functions for delaying structural damage and minimizing disruption of existing bridges to avoid traffic interruption. The dynamic response simulation results in the longitudinal direction (LD) confirmed that utilizing SMA bearings reduces curvature ductility and bearing displacement demands. Although the probabilistic assessment study in the transverse direction (TD) indicated that SMA bearings adequately reduce displacement demands, the bridge should be equipped with SLFRSs to overcome the bents’ high curvature ductility demands. Therefore, the most effective retrofit technique in TD is achieved using both SMA bearings and steel bracings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".