Three-Year Performance of Innovative Preservation Treatments to Address Pre-mature Pavement Roughness
Bibliographic record
Abstract
Alberta Transportation (AT) twinned a 27 km portion of Hwy 43:04 east of Grande Prairie in a staged sequence: subgrade construction in 2000; granular base course and first stage asphalt pavement in 2000 or 2001; final stage asphalt pavement in 2003. Within a couple of years of final stage paving, the pavement started to exhibit premature roughness characterized by heaving at low temperature transverse crack locations. AT and EBA, a Tetra Tech Company carried out an extensive forensic investigation in 2008/09 that included an evaluation of profile and IRI data collected over several years, a geotechnical investigation, and a laboratory testing program. This investigation explained the causes of the observed distresses and identified potential rehabilitation strategies. In 2009, several innovative pavement preservation strategies were constructed to improve smoothness and delay more costly major rehabilitation or reconstruction. Pavement profile data was collected during the summer and winter seasons before rehabilitation, immediately following rehabilitation, and during the summer and winters of 2010 through 2012. Based on the performance, the various treatments are ranked in terms of their effectiveness and estimated service lives. These will be used as a design input into a life cycle cost analysis as part of the next rehabilitation design. (A) For the covering abstract of this conference see ITRD record number 201402RT334E.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".