Roads Performance Modeling and Management System from Two Condition Data Points: Case Study of Costa Rica
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Initial implementation of a road management system will typically face the challenge of a lack of time-series data for performance modeling. This paper presents one approach for developing initial performance prediction models that are required to support trade-off and optimization analyses in a road management system. The paper demonstrates that starting performance models can be formulated based on as little as 2 years’ network-level data on condition and traffic. The paper builds a locally calibrated pavement condition index and subsequently uses it for network-level strategic planning and programming of works. The paper demonstrates a method of extracting initial estimates of treatments’ effectiveness from the condition data. The case study is based on the Costa Rican national road network. The model uses commercial software to allocate resources and optimize decisions. Several investment strategies were tested to investigate the sustainability of the road network value over time. The results demonstrate that optimization improves road conditions in a sustainable manner, which in the long run releases funds for other necessities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it