Québec’s Experience with Smoothness Specifications on Concrete Pavements
Bibliographic record
Abstract
Smoothness is one of the most important characteristics of pavement because it directly affects the traveling public. Furthermore, the initial smoothness reflects the quality of construction and is an essential condition for the pavement’s future performance. The Québec Ministry of Transportation (MTQ) has been using smoothness specifications for all concrete pavement projects since the early 1990s. The Profile Ride Index (PRI) was replaced in 1998 by the International Roughness Index (IRI) so the same specifications would apply for concrete and asphalt pavements. IRI specifications stated that profile measurements be made with a truck-mounted inertial profiler after the work is finished. Today, some contractors are still using the profilograph for control purposes during construction and there has been an increase in diamond grinded surface area, up to 32% on some projects. A study was undertaken in 2000 to assess different kinds of roughness measuring equipment (profilograph, Disptick©, lightweight inertial profiler, rolling profiler and 2 types of truck-mounted inertial profiler) and determine those that would be best suited for construction control. This study concluded that the rolling profiler was the best equipment for this purpose. In 2003, MTQ experimented with modified specifications requiring that roughness measurements be made using a rolling profiler and forbidding any surface correction before the measurements were made. The main benefits of this method were that MTQ could check the 100-metre control lots soon after they were built instead of waiting until the end of the work and the contractors could also use the results for own quality control.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".