Investigation of vibration data-based human load monitoring system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 it