Investigation of Piezoelectric Weigh-in-Motion Sensors’ Performance in Asphalt Concrete Pavements in Cold Temperatures of Southern Ontario
Bibliographic record
Abstract
Piezoelectric Weigh-in-Motion (WIM) sensors differ in material, structural design and installation and calibration procedures. The differences make the sensors to respond differently to equivalent loading conditions since the sensitivity of sensors to the pavement, climate and vehicle conditions are different. This research is based on performance comparison between three types of piezoelectric WIM sensors in southern Ontario in a medium strength asphalt concrete pavement (ACP) of stone mastic design under different transverse location of load (different path runs). In September 2007, three types of WIM piezoelectric sensors (ceramic, polymer and quartz) were installed at the Centre for Pavement & Transportation Technology (CPATT)’s test site at the Region of Waterloo’s Waste Management facility. Calibration and the matching procedure between the static scale located at the facility and sensors’ outputs were completed in spring 2008. At the first run of sensors’ performance comparison, this paper investigated the sensors’ responses resulted from passing a test vehicle over the sensors on different path runs. The evaluation results show that the transverse location of axle load affects significantly all piezoelectric sensors, and interaction between path run and air temperature factors affects significantly only polymer and ceramic piezoelectric sensors; however, the effects of cold air temperatures at 1.5oC level size were negligible on all sensors. Research is currently being directed to improve performance WIM sensors by improving knowledge on the effects of speed and weight of vehicle and ambient temperature specifically on the polymer sensors for cold climates such as southern Ontario’s.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".