Ontario's Move to Hot Mix Asphalt Pavement Smoothness Acceptance Using High Speed Inertial Profilers
Bibliographic record
Abstract
The Ministry of Transportation Ontario (MTO) implemented a smoothness specification for newly constructed hot mix asphalt paving in 1997. Up until 2010, the smoothness of new asphaltic pavements was accepted based on Profile Index (PI), measured by California profilographs. These 7.6 m long manual profilographs are operated at walking speed, measure one wheel path at a time and require lane closures. Therefore, MTO and the Ontario hot mix asphalt industry sought an alternative device that would be faster and safer to operate, alleviating the need for lane closures. MTO adopted the ProVAL software program to calculate International Roughness Index (IRI) and identify localized roughness. This paper presents the transition from the TxDOT algorithm in ProVAL to the Smoothness Assurance Module (SAM) of ProVAL version 3 to identify localized roughness. In the 2010 and 2011 construction seasons, MTO conducted extensive analyses in collaboration with the Ontario hot mix asphalt industry to develop comparable acceptance thresholds between the two algorithms. From these analyses, a two-category payment reduction system was developed, to replace the former three-category system, for localized roughness. This paper reports on studies supporting the transition from California profilographs to high speed inertial profilers, and the analysis that led to development of acceptance criteria for localized roughness based on the ProVAL Smoothness Assurance Module. The paper also provides an overview of the challenges that were faced during implementation of high speed profilers and how they were overcome. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".