A New Pulse-Based E-R-µ Method for Predicting the Peak Seismic Response of Highly Nonlinear Bridge Structures
Bibliographic record
Abstract
Simplified analysis methods used in AASHTO, CAN/CSA-S6 and EC8 codes for the seismic design of isolated structures typically rely on a linearization of nonlinear systems and assume that the peak response can be obtained from a steady-state dynamic response centered about the origin of the force-deflection response of the system. This assumption, while adequate for a certain range of nonlinear systems, leads to potentially large inaccuracies, especially for highly nonlinear systems. The article presents a new pulse-basedE-R-µmethod that was developed to overcome these limitations and achieve better peak response predictions. The method is based on the response of nonlinear systems to single acceleration pulses which more realistically reflects the effects of ground motions on seismically isolated structures. In addition, the method does not require iterations, which represents a major advantage compared to current iterative linearization approaches. In the article, assumptions of current code methods are summarized. Energy based concepts forming the basis of the newE-R-µmethod are introduced. The method is then described and validated against the results from nonlinear time history analyses for a large number of isolated bridge models. The method is also applied and validated for an archetype isolated bridge case. For this structure, the proposedE-R-µmethod is found to give an upper bound prediction of the displacement demand that is obtained from nonlinear dynamic analysis.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".