A crowdsourcing-based methodology using smartphones for bridge health monitoring
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
This article presents a novel framework for monitoring and evaluation of a population of bridges using smartphones in a large number of moving vehicles as mobile sensors. Within this framework, a damage detection methodology based on Mel-frequency cepstral coefficients and Kullback–Leibler divergence is developed. For this method, Mel-frequency cepstral coefficients of the vibration data collected from smartphones in vehicles crossing bridges are first extracted as features. Then, Kullback–Leibler divergence is used to compare the distributions of features. The damage in a bridge can be identified by quantifying the difference of the distributions obtained for the same bridge. Both numerical and lab experiments are conducted to verify the proposed framework and methodology. In lab experiments, a smartphone and two wireless accelerometers are used for data collection. From our results, it is concluded that the damage existence can be successfully identified using smartphones in a large number of vehicles. Also, it is observed that there is a significant correlation between the magnitude of the damage features and the severity of damage. The results show that the method has the potential to monitor a population of bridges simultaneously and in almost real time.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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