Experimental study of human-induced dynamic forces due to bouncing on a perceptibly moving structure
Bibliographic record
Abstract
This paper describes the first direct measurements of human-induced dynamic forces due to bouncing on a perceptibly moving force platform. A unique test rig, permitting a person to bounce physically on an idealized "single-degree-of-freedom system" with variable natural frequency and mass, is described and the test methodology explained. A set of representative results for different structure and bouncing frequencies is presented for one test subject. These results clearly demonstrate the effect that the flexibility of the structure has on the levels of force and dynamic response achieved. In particular, it was established that the applied force drops considerably when the subject bounces at a frequency fairly close to the natural frequency of the structure. However, it was found that it was physically not possible to bounce at or very close to the natural frequency for the configuration of the test rig chosen. Finally, the acceleration and displacement responses indicate that both the first and second harmonics of the bouncing force are capable of producing a near resonant response.Key words: crowd loading, flexible structure, dynamics, human-structure interaction, bouncing, jouncing, bobbing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".