Development of an integrated platform for pavement rehabilitation design optimization
Bibliographic record
Abstract
This thesis develops an integrated and efficient approach to evaluate pavement surface distress and riding comfort on road sections of a municipal network in order to determine the most likely cause of deterioration, suggest relevant tests to confirm the diagnosis and determine the most appropriate interventions on technical and economic grounds. \n \nThe thesis then applies that framework to the evaluation of the road network of the City of Chateauguay. All the distress present on the surface of all the road network are assessed alongside the riding comfort. The preliminary diagnosis regarding the most likely cause of deterioration associated to every road section is provided, followed by the relevant tests to perform to confirm the preliminary diagnosis for all the road sections of the network. The determination of the most relevant type of intervention is also provided from three main options depending on the cause of failure. A preliminary structural design is also provided to address highly trafficked roads for every type of subgrade. \n \nA detailed cost evaluation of every intervention option is provided, taking into account materials and site execution processes. User cost savings are projected over the life of the pavement based on projected routine and periodic maintenance needs established by the Ministry of Transport of Quebec. The estimated internal rate of return is provided for every candidate section, based on its roughness, distress condition, traffic levels, maintenance projections and cost of materials.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".