Collective influence of autonomous trucks and climate change on asphalt concrete pavement performance
Bibliographic record
Abstract
Autonomous vehicles (AVs) movement in a narrower traffic lane and anticipated climate change are crucial for asphalt concrete (AC) pavement distress. This study assesses the combined effect of AVs and climate change on the performance of AC pavement for a road section in Ontario, Canada. The performance of AC pavement due to AVs and climate change has been evaluated using the AASHTOWare Mechanistic-Empirical (ME) pavement design. AVs were incorporated in ME pavement design using traffic factors such as adjusting traffic volume with the load equivalency and lane distribution factors. This analysis was carried out to determine the individual and combined influence of AVs and climate change on pavement performance. This study determines the combined impacts of AVs and climate change by comparing pavement performances for human-driven vehicles with historical climate and AVs with projected climate. The comparative performance analyses of human-driven vehicles and AVs with projected climate demonstrated the effect of climate change. AVs and climate change combinedly and AVs alone accelerate the accumulation of AC rutting and bottom-up fatigue cracking. The regulation of AVs explicitly to ensure uniform loading distribution and the placement of AVs with non-AVs in a controlled manner were the best alternatives to minimise pavement distress.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".