Resilience-based optimization model for maintenance and rehabilitation of pavement networks in a freeze–thaw environment
Bibliographic record
Abstract
According to the Canada infrastructure report 2016, 62.6% of roads in Canada are in a good condition, nevertheless, with current investment rates, significant road networks will suffer a decline in their condition and will be vulnerable to sudden failure. Accordingly, this paper tackles the pavement resilience from an asset management perspective and aims at developing a resilience-based asset management framework for pavement networks. This was carried out through the development of five components: (i) a central database of asset inventory, (ii) a pavement condition and level of service assessment models, (iii) a regression of the effect of freeze–thaw on pavement network, (iv) a financial and temporal models, and (v) an optimization model to formulate the mathematical denotation for the proposed resilience assessment approach and integrate the above components. The model results were promising in terms of maintaining pavement resiliency by selecting a near optimal intervention plan that meets the municipality limitations.
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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".