Evaluating the structural performance of eRoad pavements: impact of inductive charging coils on mechanical behaviour
Bibliographic record
Abstract
Electric Road Systems (ERS) are pivotal in advancing Electric Vehicle (EV) technology by enabling dynamic wireless charging through integrated elements in roadway infrastructure. These systems extend EV range and reduce the need for frequent recharging, while supporting smaller batteries. In contactless ERS, inductive coils are embedded in pavement layers, leaving the road surface unaltered, unlike ground-based conductive systems. However, the long-term effects of embedded coils on the mechanical response and integrity of pavement structures, particularly under Canadian climatic and traffic conditions, remain underexplored. This study evaluates the mechanical performance of electrified road (eRoad) pavements with inductive coils, compared to traditional pavements (tRoad). Sensor-based monitoring assessed the performance of three full-scale pavement structures (two eRoad and one tRoad) built at Laval University's accelerated pavement test facility, under varying loads and environmental conditions. Results show different strain distribution in eRoad pavement, suggesting possible bonding issues between coil casing material and asphalt concrete.
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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 | 0.000 |
| 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) | 0.001 | 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".