A numerical modelling framework for vibration assessment of timber composite floors in mass timber buildings
Bibliographic record
Abstract
Timber composite floors are vulnerable to human-induced vibrations due to their low weight and long spans used in office buildings. Introducing concrete into timber panels is a common approach to enhance the vibration performance of long-span timber floors. While the effects of certain parameters on the vibration performance of timber composite floors have been extensively studied in laboratory settings, and some numerical models have been proposed, predictions are often sensitive to variations in input parameters. Many of these numerical models are “calibrated” using test data from specific experiments (e.g., connection or 4-point bending tests) conducted on specific laboratory floors and may not be applicable to real building floors. This paper presents a comprehensive physics-based finite element (FE) modelling framework aimed at accurately predicting the vibration characteristics (i.e. frequency and acceleration) of long-span Timber Concrete Composite (TCC) floors and understanding the vibration response of composite floors. The accuracy of the approach is examined by comparing modelling predictions against test data for a 9 m (∼30 ft) composite floor within a real office building. The application of analytical equations for predicting floor static stiffness, and frequency, and limitations of simple approaches suggested in some standards are discussed. The developed framework is shown to be a valuable tool for benchmarking the impact of various boundary conditions and input parameters recommended in design guides. Specifically, the effects of key parameters, including the dynamic modulus of concrete, shear stiffness of glulam beam-to-CLT and CLT-to-concrete connectors, and the stiffness of beam-to-beam connections are demonstrated and discussed. • Numerical framework to study static and dynamic behaviour of timber composite floors. • Application of analytical equations for predicting floor stiffness and frequency. • Model validation employing experimental data from walking tests on a real floor. • Parametric studies to quantify the role of various design parameters. • Significant impact of adjacent bays and connections on floor's acceleration response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".