Performance of Weigh-in-Motion (WIM) Sensors in Rigid and Flexible Pavements and Guidelines for Recommended Pavement Thickness
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
The performance of a WIM site mainly depends on sensor technology, pavement conditions, calibration, and maintenance practices. An adequate pavement structure is required to install and accommodate WIM system sensors throughout their service life. WIM sensor manufacturers suggest that the plate-based sensors [load cells (LC) and bending plate (BP)] should only be installed in Portland cement concrete (PCC) pavements, while the linear or strip type sensors [such as polymer piezo (PP) or piezo cable (PC), and quartz piezo (QP)] could be installed on both PCC and asphalt concrete (AC) pavements. This paper evaluates the influence of pavement surface thickness on WIM accuracy data for different sensor types and suggests adequate thicknesses for WIM stations installed in PCC and AC pavements based on the data. Data from ninety-four (94) WIM stations in the United States and Canada are used for WIM accuracy and pavement thickness analyses. For 18 sites, BP sensors are installed in PCC pavements. Out of 29 total QP sensor sites, 6 and 23 had PCC and AC pavements. In contrast, 19 PC sites have PCC pavements, and the remaining 28 sites have AC pavements. The results show that BP sensors can be installed in 10 in. or thicker PCC slabs to yield ASTM type I accuracy. Irrespective of pavement type, 8 in. or above (PCC or HMA thickness) is recommended for QP sensors to obtain highly accurate WIM data. No consistent trends were observed for PC sensors, as the sites showed significantly higher gross vehicle weight error even after calibration in both AC and PCC pavements.
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