A Crowdsensing-Based Framework for Indirect Bridge Monitoring Using Mel-Frequency Cepstral Analysis Considering Elimination of Operational Effects
Bibliographic record
Abstract
This paper puts forward an indirect bridge monitoring method using Mel-frequency cepstral analysis of inverse-filtered drive-by acceleration signals collected through smartphones. Crowdsensing-based approaches using data collected by smart cars and smartphones opened a new chapter in bridge monitoring by reducing the costs and increasing the efficiency of the bridge monitoring process. However, the major challenge of the dominancy of the operational effects in the recorded drive-by vibrations overshadows the bridge monitoring objective. This paper proposes an inverse filtering-based monitoring approach to suppress operational effects. The inverse-filtered spectrum is later employed in a Mel-frequency cepstral analysis, leading to the calculation of the abnormality index, which is then used to detect the change in the bridge state. The performance of the proposed method in suppressing operational effects is assessed through a series of laboratory and real-life experiments. Afterward, the damage detection capability of the method is investigated for two damage levels at different locations along the bridge, modeled in a laboratory environment. The results provide evidence for the capability of the proposed method in drive-by damage detection of bridges. Moreover, using the smartphone as the data acquisition device paves the path toward the implementation of the method for crowdsensing-based bridge monitoring in future smart cities, although more operational factors such as passenger interactions and resulting smartphone motions need to be considered in future studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".