Evaluating Crowd Induced Dynamic Loads through Field Measurement and Analytical Methods
Bibliographic record
Abstract
Crowd behavior during Lively Concerts and sporting events may excite the supporting structures. Even more extreme, Exercise Concerts (similar to a Zumba® Fitness Concert) can have music beats approaching 3 Hz, or 180 beats per minute. This frequency range is likely to exceed the rhythmic activity design parameters typically assumed by designers. Owners and facility operators may be concerned about the life safety and comfort of patrons engaged in such activities. On a recent project, we investigated an existing structure for crowd induced dynamic loading from a “Fitness Concert” for an owner. The project involved a floor structure comprising existing precast double-tee beams supported on inverted tee-girders. We conducted field measurements in order to verify the dynamic properties of the structural system. In order to do simple infield measurements of the floor structure, an iPhone was used to generate acceleration-time history data from an application using the phone’s built-in accelerometers. We verified the iPhone for this type of application with a simple test setup in the office. We used the field measurements with published analytical methods referencing the PCI Design Handbook, the National Building Code of Canada, and AISC Design Guide 11. We computed equivalent static loads (ESL) for a range of forcing frequencies to compare the dynamic loading with the design capacity of the precast double-tee. The study determined that the precast double-tee beams were suitable for typical concert crowd loads. However, the response from Exercise Concerts exceeded the typical human perception tolerance levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".