Mechanistic-Based Nondestructive Structural Asset Management Testing to Optimize Low-Volume Road Structural Upgrades
Bibliographic record
Abstract
The Saskatchewan, Canada, Ministry of Highways and Infrastructure is investigating integrated structural asset management to help optimize investment in the rural low-volume road (LVR) network. Integrated ground-penetrating radar (GPR) and heavyweight deflectometer (HWD) testing were found to be very effective structural assessment tools that might be used to strategically rehabilitate, maintain, and upgrade Saskatchewan's LVR network, which accounts for 80% of the ministry's total network. This paper demonstrates this integration at a project level to assess the pre- and postconstruction structural condition of two LVRs in Saskatchewan. The preconstruction GPR survey applied in this study showed locations of trapped moisture within the road structure's granular materials. The postconstruction HWD assessed the end product structural integrity of the road after its rehabilitation treatment. The ability to strategically allocate limited financial resources across the extensive in-service LVR system in Saskatchewan on the basis of accurate structural asset management infrastructure performance data was essential for this project, given the high variability in Saskatchewan LVR structures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".