Continuous primary dynamic pavement response system using piezoelectric axle sensors
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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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".