Exploring the relationship between earthquake intensity and building damage using single and multi-degree of freedom models
Bibliographic record
Abstract
The correlation between intensity and damage measure for generic moment-resisting steel frames (MRSF) that are subjected to earthquake shaking was analysed using optimised nonlinear single degree of freedom (SDOF) models. The parameters for these SDOF models are based on nonlinear quasi-static pushover analyses of the multi-degree of freedom (MDOF) building systems. This intensity–damage relationship was then compared with those obtained from incremental dynamic analysis (IDA), that is a MDOF approach. Results show that the general trend line of intensity–damage measures for nonlinear SDOF model was qualitatively similar to the MDOF model that in this case represents a full nonlinear finite element analysis of the MRSF. While the SDOF model is not a precise match to the MDOF model, results suggests that it can be used in the vital role of ground motion record selection because of its computational efficiency. Furthermore, it is shown that the accuracy of intensity–damage estimates from the SDOF models can be improved if optimal damping, pseudo-yield, and ultimate loads are chosen. The efficacy of various intensity measures on damage are compared and contrasted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".