Experimental evaluation of fundamental frequencies of buildings
Bibliographic record
Abstract
ABSTRACT The application of dynamic testing to civil engineering structures provides a means to understanding a structure’s dynamic properties. Such testing includes Forced Vibration Test (FVT), Earthquake Response Test, Ambient Vibration Testing (AVT), and Free Response Test. AVT has become a popular dynamic testing methodology due to its practical and economical advantages. In this research, the feasibility of employing AVT to obtain reliable fundamental frequencies of buildings was studied by performing AVT inside the Burnside Hall building of McGill University. All measurements were taken by two pairs of CityShark II Microtremor Acquisition and velocimetres. Three system identification approaches, direct Fourier Transform, Power Spectrum Density, and Auto-Regressive-Moving-Average were studied and compared. It is recommended to use both the direct Fourier Transform and the Auto-Regressive-Moving-Average to extract frequency contents from AVT data. Use of the Power Spectrum Density could easily filter out important frequency content because AVT signals typically have small amplitude. The lowest translational frequency of the Burnside building was identified to be 1.4 Hz, and the lowest rotational frequency was identified to be 2.2 Hz. Translational modes were best obtained by placing sensors near the centre of rigidity, and rotational modes were best obtained by placing sensors at corners of the building. In addition, the amplitude of the modal peaks increased as the floor elevation increased. Strong wind amplified the fundamental modes, whereas mild wind excited all modes almost equally. The fundamental periods calculated from the 1995 and 2005 NBCC were larger than that obtained from the experimental AVT period. This shows that non-structural components could contribute significantly to the stiffness of a building when the excitation is small.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".