Real-Life Investigations of Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This paper puts forward a real-life assessment of a novel inverse filtering methodology to extract bridge features from acceleration signals recorded on smartphones in the passing vehicles. The vibration of a moving vehicle is affected by various features, such as suspension and speed. This study focuses on filtering out these effects from the signals to extract bridge frequencies as the vehicle crosses the bridge. Hence, the spectrum of the vibration data recorded on the vehicle when moving off the bridge is employed to form an inverse filter which removes the vehicle-related frequency content. Since the speed of the vehicle is found to be one of the most effective factors in the filter design in our previous studies, a database of the off-bridge vibrations is built for different speeds. Later, when the same vehicle is moving on the bridge, the corresponding inverse filter is applied to the recorded on-bridge data to suppress the vehicle frequencies and amplify the bridge frequencies. All the required data are recorded using the built-in accelerometer and GPS sensor of the smartphone, eliminating the need for any extra instruments. In addition, this approach considers each data source separately and designs a unique filter for each data collection device within each vehicle, which makes it robust against device and vehicle features. As a result of the proposed methodology, it would be possible to monitor a large number of bridges using crowdsourced data collected from the smartphones in the vehicles. Such methodologies are expected to improve the sustainability and resiliency of our future infrastructure systems and future cities.
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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