A Low-Cost Method to Develop an Initial Pavement Management System in One Year
Bibliographic record
Abstract
Implementation of a pavement management system requires data collection to estimate system needs, performance modeling to forecast time sensitive changes and decision making to allocate interventions. Many agencies have embarked in the implementation of a management system which eventually renders its fruits; however, implementation typically involves expensive equipment, years of data collection and hundreds of hours of workmanship. This all results in a barrier that impedes implementation at small municipalities and governments in developing countries. This paper reveals the secrets to develop and implement a low cost pavement management system in one year. First a pavement roughness indicator was estimated using mobile technology and android applications from vertical accelerations normalized by speed. Performance curves and treatment effectiveness were estimated to match locally observed data following previous research results. A case study of the town of Saint-Michelle in Quebec demonstrated the method. Investment scenarios showed that $254,418 dollars are required to sustain current levels of condition, which are poor. A budget of $350,000 dollars was needed to achieve increasing levels of service to reduce roughness to about 1.8 m/km after 18 years. Total annual funding dropped to about $150,000 dollars after 18 years of full budget investment, when the system allocates most of the budget in preventive maintenance, releasing funds for other government needs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".