On the application limits and performance of the single-mode spectral analysis for seismic analysis of isolated bridges in Canada
Bibliographic record
Abstract
Single-mode spectral analysis (SMSA) is a simple procedure efficiently used to evaluate the seismic demands of base-isolated bridges, particularly suitable for the design of simple bridges or preliminary design of complex bridges. However, bridge design codes, notably the Canadian code CSA-S6:19 (CSA-S6 2019), specify many limitations on the use of the method for final design. This paper evaluates the performance of SMSA and the efficiency of its limits of application as specified in the current codes through the results of a parametric study. The hysteretic properties of seismic isolation systems and the stiffness of bridge substructures are varied. Seismic demands predicted by SMSA and nonlinear time-history analyses (NLTHAs) are then compared, both inside and outside the current specified limit range in CSA-S6:19 (CSA-S6 2019). Results show that the most effective application conditions are those related to the maximum equivalent viscous damping and minimum restoring force. The upper limits of the effective period and the post-elastic period can be ignored. Further, to complement SMSA, a relation is proposed to estimate the expected residual displacement as a function of the restoring force at the design displacement. Regression relations allowing estimating the expected mean and confidence interval of the relative displacement deviation predicted by SMSA from that based on NLTHA are also proposed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".