A Framework for Smart Pavements in Canada
Bibliographic record
Abstract
Maintaining an acceptable durability and satisfactory in-service condition for pavements is a crucial and relatively complex task, which otherwise can have considerable economic, environmental, and social consequences. Design and management of pavements have traditionally relied mainly on empirical models. However, pavements have been undergoing drastic changes, especially during the new millennium, which can compromise the reliability of the empirical models which were developed based on relatively stagnant historical data. Climate change, traffic loading growth and advancements in pavement materials are some of the main drivers of moving towards more mechanistic-empirical methods which would allow for a better understanding of pavement performance evolution in the future. To this end, this paper discusses the opportunities and challenges of a proposed framework for developing smart pavements in Canada, as well as a summary of the efforts that so far have been made in this regard. The goal of the study is to enable autonomous monitoring and data collection from the instrumented pavement sections in a suitable manner to allow for training Artificial Intelligence models, improving interpretation of the pavement responses and, ultimately, future pavement performance predictions.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".