Seismic Response Prediction of Bridges Using Incremental Dynamic Analysis with Subduction Zone and Crustal Ground Motion Records
Bibliographic record
Abstract
Typically only the ground motion records from crustal earthquakes have been used in practice for seismic performance assessment of bridges. For some sites, such as Vancouver and Seattle, subduction earthquakes (i.e., interface, and inslab events) with very different characteristics (e.g., spectral content and duration) can occur. The effect of using ground motion records from different earthquake types on the seismic response predictions for a continuous 4-span reinforced concrete bridge located in Vancouver is investigated. The bridge was designed according to the current Canadian seismic provisions. The seismic response of the bridge was investigated using Incremental Dynamic Analysis (IDA). IDA was carried out separately for records selected from three different earthquake sources including shallow crustal events, interface (megathrust) and deep inslab subduction earthquakes. The median structural capacities, in terms of spectral acceleration, were predicted at different damage states of columns including, yielding, cover spalling, bar buckling and collapse for three different earthquake types separately. The sensitivity of the IDA results to the record selection methodology used, including the conditional mean spectrum (CMS)-based record selection, was also studied. The CMS was developed using the seismic deaggregation results for Vancouver.
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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 | 0.001 | 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".