Rolling Resistance Evaluation of Pavements Using Embedded Transducers on a Semi-Trailer Suspension
Bibliographic record
Abstract
Road agency initiatives to reduce traffic-related greenhouse gas emissions are limited by the inability of current experimental methods to assess pavement impacts on vehicle energy consumption. This study addresses this by examining the rolling resistance of a semi-trailer suspension under highway conditions using a precise measurement system with embedded transducers. Data were collected over 174 km of highway, covering various pavement types under mild summer conditions. The analysis revealed notable differences in rolling resistance due to pavement characteristics, with more pronounced variations observed within pavement types than between them. For instance, geographically consecutive jointed rigid pavements showed a 34% variation in rolling resistance, likely correlated with harmonic excitations generated by slab presence, while flexible pavements exhibited up to a 21% variation under similar tire operating conditions. Composite pavements generally performed the worst, possibly due to interactions between bituminous materials and older cement-based foundations. The study also highlighted the critical role of tire operating conditions, showing a decrease of 0.09 kg/tonne in rolling resistance for every 1 °C increase in temperature. This research shows that precisely measuring the rolling resistance (±0.1 kg/tonne) in situ for heavy vehicles is feasible and underscores the need for additional data in diverse weather scenarios to better align laboratory results with on-road realities.
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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".