PERFORMANCE-BASED SEISMIC RETROFITTING OF BRIDGE BENTS WITH ENGINEERED CEMENTITIOUS COMPOSITES
Bibliographic record
Abstract
A performance-based seismic design approach is implemented to achieve the desired structural performance over a wide range of seismic hazard levels. It requires defining a set of targeted performance levels and their corresponding limits. Current codes and guidelines do not prescribe such limits for different performance levels for existing bridges with seismic deficiencies such as inadequate ductility and low shear strength. In this study, quantitative damage states expressed in terms of limiting drifts at various performance levels will be developed considering the engineered cementitious composite (ECC) jacket as the retrofitting technique. Considering a seismically deficient bridge bent located in Vancouver, BC, Canada, this study aims to develop a performance-based seismic retrofit method using an ECC jacket. Three earthquake sources: crustal, intra-slab, and interface will be considered due to the location of the bridge. Incremental Dynamic Analysis (IDA) will be conducted for developing the performance-based damage states considering different performance and service levels following Canadian Highway Bridge Design Code (CHBDC). The developed damage states will be used for the performance-based seismic retrofit design of a seismically deficient bridge bent. Fragility analysis will also be performed to obtain the conditional probabilities of exceeding the targeted performance levels. The outcomes of this study will aid in the seismic retrofit design of deficient bridge bents following a performance-based design approach.
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 | 0.000 | 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) | 0.000 | 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 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".