Modified three‐dimensional seismic assessment method for buildings based on ambient vibration tests: extrapolation to higher shaking levels and measuring the dynamic amplification portion of natural torsion
Bibliographic record
Abstract
Summary This paper presents applications of the modified 3D‐SAM approach, a three‐dimensional seismic assessment methodology for buildings directly based on in situ experimental modal tests to calculate global seismic demands and the dynamic amplification portion of natural torsion. Considering that the building modal properties change from weak to strong motion levels, appropriate modification factors are proposed to extend the application of the method to stronger earthquakes. The proposed approach is consistent with the performance‐based seismic assessment approach, which entails the prediction of seismic displacements and drift ratios that are related to the damage condition and therefore the functionality of the building. The modified 3D‐SAM is especially practical for structures that are expected to experience slight to moderate damage levels and in particular for post‐disaster buildings that are expected to remain functional after an earthquake. In the last section of this paper, 16 low to mid‐rise irregular buildings located in Montreal, Canada, and that have been tested under ambient vibrations are analyzed with the method, and the dynamic amplification portion of natural torsion of the dataset is reported and discussed. The proposed methodology is appropriate for large‐scale assessments of existing buildings and is applicable to any seismic region of the world. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".