Investigation of vibration data-based human load monitoring system
Bibliographic record
Abstract
Structural design of flexible footbridges requires a thorough understanding of pedestrian-induced vibration such that their dynamic behavior is accurately predicted. Human-induced vibration creates complicated ground reaction forces that contribute to human–structure interaction in the footbridges. It, therefore, becomes a significant challenge to the bridge designers to accurately estimate the moving pedestrian load on the footbridges during the design phase. This article examines the issues of human–structure interaction in slender pedestrian bridges and aims to analyze the walking pattern of the pedestrian from human-induced vibration data of the bridge. A wavelet-based time–frequency decomposition technique is adopted to extract the walking pattern of the pedestrian followed by time-series analysis of the walking pattern to develop a statistical model of pedestrian-induced vibration. Full-scale testing is conducted on a footbridge to validate the proposed technique under a wide range of pedestrian excitation. An experimental study is conducted to demonstrate the proposed method using the pedestrian’s walking on a force plate monitored by video cameras and vibration sensors. Identified walking patterns are then compared with the actual walking patterns measured by motion sensors attached to the test subject.
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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".