SYSTEM IDENTIFICATION OF TALL MASS TIMBER STRUCTURES EMPLOYING AMBIENT VIBRATION TEST AND FE MODELLING
Bibliographic record
Abstract
Despite the recent rapid development in dynamic characteristics identification of structures, lack of knowledge in dynamic properties of tall mass timber buildings is still an open issue for researchers and designers.There are ongoing international efforts to develop a comprehensive database for predicting the vibration performance of timber structures for serviceability and seismic design.This paper discusses an ambient vibration test (AVT) that was conducted on a six-storey mass timber building known as Wood Innovation and Design Centre (WIDC) located in Prince George, Canada.The test results including the experimental natural frequencies and damping ratios were compared with a threephase test program undertaken in 2014, 2015, and 2017 by FPInnovations.In addition, a numerical modal analysis was conducted on the same building, using both simplified and complex finite element (FE) models.A sensitivity analysis was carried out considering various assumptions of connection types to investigate its effect on the natural frequencies of the structure.The results of current AVT showed minor changes in frequencies over the service time in comparison to previous tests.According to the numerical results, the simplified FE model poorly matched with the results from AVT, while the complex model showed a better agreement with the measured fundamental frequency; however, significant discrepancies were observed in the second and third modes.The sensitivity analysis indicated low impact of different connection type assumptions on the natural frequencies of the case building obtained from the FE models.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".