City of Saskatoon’s Pavement Management System: Network-Level Structural Evaluation
Bibliographic record
Abstract
Pavement management systems (PMS) combine economics and engineering to derive cost-effective solutions for road maintenance and reconstruction. Since 1993, the City of Saskatoon (COS) has employed a PMS that focuses on pavement surface deterioration and ride quality to measure the performance of the city’s road network. However, reliably predicting the structural condition of roads based on surface distress information can be very difficult; furthermore, structural road issues are the most intensive and costly to rehabilitate. The COS started using heavy weight deflectometer (HWD) measurements to assess the structural condition of the COS road network in 2006. Since it is difficult to distinguish between certain surface distresses, like top down cracking, from structural distresses, such as fatigue cracking, HWD structural information may be beneficial in assessing the condition of the road structure and the corresponding treatment needed. Therefore, using COS network level PMS surface distress data and condition ratings, the effect of using structural data as measured by HWD is examined in this paper. Two neighborhoods in Saskatoon were analyzed using typical COS PMS surface distresses and HWD deflection measurements. The results of structural condition assessments complement and enhance the findings of surface condition assessments. The risks posed by using surface condition assessments can be mitigated by using structural condition assessments in addition to surface condition assessments. Ultimately, the use of structural asset management will reduce the risk of a significant road failure and subsequent high expenditures to fix such a failure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".