MICROTREMOR MEASUREMENTS AND NUMERICAL MODELLING OF A TALL TIMBER-CONCRETE HYBRID BUILDING
Bibliographic record
Abstract
Timber used as a structural material in high-rise buildings could add valuable bio-based alternatives for the development of sustainable cities. Particularly, timber-concrete hybrid (TCH) structures represent an efficient and practical solution to attain target structural performance, such as serviceability and seismic performance code requirements, by combining elements made of timber and concrete. The vibration properties—natural frequencies, damping ratios, and mode shapes—are fundamental parameters in the lateral design of buildings; however, these data are limited at this time for high-rise TCH buildings. In this research, microtremor measurements of an 18-story TCH building (UBC Brock Commons, Canada) were performed, and its vibration properties were identified via a stochastic subspace identification (SSI) procedure. A finite element (FE) model of the building was developed based on the structural design information to estimate its natural frequencies and mode shapes, and to perform modal time history analyses. The experimental results showed that the damping ratios remain between 1% and 3%, up to the eighth mode, and a 10% larger fundamental lateral period is estimated using the empirical formula provided in the 2020 National Building Code of Canada. The numerical study showed that a 2% constant damping ratio could provide a reliable estimation of the building's seismic response. In addition, the non-structural components significantly affected the fundamental frequency and the inter-story drift response in the longitudinal direction of the reference building. The findings provide practitioners with an insight into the vibration properties of high-rise TCH buildings and pave the way for developing reliable numerical models for the seismic design of TCH structures.
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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 | 0.000 |
| 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) | 0.000 | 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".