Story‐by‐story estimation of the stiffness parameters of laterally‐torsionally coupled buildings using forced or ambient vibration data: I. Formulation and verification
Bibliographic record
Abstract
SUMMARY A new parameter estimation algorithm is described for identifying the stiffness properties of torsionally coupled shear buildings from their linear response due to ambient excitations or during low‐amplitude forced‐vibration tests. The algorithm is based on the time‐domain equations of motion, and yields estimates of the stiffness properties using a measure of the equilibrium of forces acting on each floor over a time interval. The banded structure of the stiffness matrix — a property intrinsic to torsion‐shear buildings — is exploited to decompose the initial inverse problem into several problems of reduced size. This decomposition allows the identification of lateral and torsional stiffnesses of individual stories, independent of the others. The algorithm utilizes vibration data where input excitation is known/measured, which is typical for forced‐vibration tests and earthquakes. If the ambient vibrations of the structure are adequately uncorrelated to the (unknown) external forces that induce such vibrations, then the algorithm can also be modified for output‐only system identification. The proposed algorithm is verified — and its various attributes are investigated — using simulation data from the ‘Analytical Phase I’ of the IASC (International Association for Structural Control)‐ASCE (American Society of Civil Engineers) benchmark studies. The companion article is devoted to the algorithm's application to experimental data, using data from the ‘Experimental Phase’ of the same benchmark studies. Copyright © 2011 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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".