Continuous primary dynamic pavement response system using piezoelectric axle sensors
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
Increasing commercial traffic over recent years is inflicting increased damage to roadways. As a result, road engineers are adopting more mechanistic performance-based road-modeling techniques to assist in the design, construction, and preservation of road assets. One such common mechanistic analysis technique is dynamic deflection pavement response induced under typical commercial truck loading. This paper presents an investigation of piezoelectric axle sensors as a possible tool for obtaining dynamic pavement deflection data under commercial truck loadings. One of the primary benefits to using piezoelectric axle sensors is that there are thousands of piezoelectric sensors already installed in roads world wide currently measuring the dynamic weights of commercial vehicles. Specifically, this research investigated the potential to use several different types and orientations of commercially available piezoelectric axle sensors to measure pavement deflection response under heavy truck loading. This research found that data from certain piezoelectric sensors and configurations could potentially predict deflection characteristics of a typical flexible pavement system. Based on these findings, there is the potential to use piezoelectric axle sensors for primary response modeling of road structures.Key words: piezoelectric sensors, deflection bowl, weigh-in-motion, mechanistic road modeling.
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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 | 0.001 | 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