Assessment of pavement structure using multi-source data from a moving vehicle
Bibliographic record
Abstract
In this paper we propose a novel method for assessing pavement structure based on multi-source data from a moving vehicle. The classification of pavement on subgrade (POS) and pavement on bridge deck (PBD) was used as examples. A mobile acquisition system was designed to collect multi-source data. Vehicle accelerations on PBD and POS were analyzed, and some features were selected to build 15 Support Vector Machine classifiers. Our results show that some features are helpful, including vehicle speed, maximum value, minimum value, standard deviation of acceleration in the time domain, and three peak frequencies in the frequency domain. The accuracy was 91.67%. Then, some sections were tested with a falling weight deflectometer to verify the structural differences between POS and PBD. Our results provide a novel method for analyzing pavement structure via vehicle vibration, and this work will be built on for more detailed analysis of pavement structure in the future.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".