EVALUATING THE SEISMIC PERFORMANCE OF BRIDGES IN METRO VANCOUVER CONSIDERING DEEP BASIN EFFECTS
Bibliographic record
Abstract
This study is a part of the broader project aimed at assessing the seismic resilience of the transportation network in Metro Vancouver, BC. This paper evaluates the seismic performance of bridges within the Metro Vancouver under plausible M9 Cascadia subduction zone (CSZ) earthquakes using a simplified modeling approach. An inventory of more than 200 bridges was assembled by collecting as-built drawings. The detailed properties of approximately 80 bridges were extracted, with the focus on bridges supported by reinforced concrete (RC) circular columns and rectangular walls. By leveraging this information, simplified Single-Degree-of-Freedom (SDOF) bridge models were developed to characterize the mass, stiffness and strength of each bridge. Nonlinear time history analyses were carried out to evaluate the response of each bridge under 30 physic-based ground motion simulations of M9 CSZ earthquakes adjusted for the corresponding site conditions, which explicitly capture the amplification effects of the Georgia Sedimentary Basin. The results indicate that bridge damage correlates well with basin depth, with 33%, 48% and 92% of probability of complete damage, on average, for bridges outside the basin, in the basin edge and in deep-basin sites, respectively. While modern bridges perform considerably better in outside-basin sites, their performance is comparable to older bridges at basin-edge and deep-basin locations.
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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".