Monitoring Populations of Bridges in Smart Cities Using Smartphones
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
Continuous bridge sensing and monitoring is an important component of smart infrastructure. Traditional bridge monitoring techniques require sensors to be installed on bridges, which is costly and time consuming. Also, a certain set of sensors have to be used to monitor a single bridge at a time. In order to resolve these issues, a novel bridge damage detection method focusing on monitoring a population of bridges simultaneously utilizing crowdsourcing data collected from smartphones on passing-by vehicles is developed. In this method, Mel-frequency cepstral coefficients (MFCCs) are first extracted on the acceleration data collected from smartphones in all the vehicles within a certain period. Principal component analysis (PCA) is used to transform the features so that they are linearly uncorrelated. The damage is then identified by comparing the distributions of these transformed features. The results from lab experiments show that the approach not only identifies the existence of the damage, but also provides useful information about severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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