Integration of GIS and GPS as a decision support tool in a GAMS-based network-level pavement maintenance optimisation system
Bibliographic record
Abstract
The transportation system is crucial for a country's growth, with road transport connecting villages and cities, especially in developing countries like India. Neglecting road maintenance leads to severe pavement impairment and reconstruction expenses, affecting the economy. Government challenges include deteriorating urban roads and inadequate funding, resulting in subjective, ad hoc maintenance decisions. An effective pavement management system is essential for optimal maintenance and rehabilitation. This study assesses urban road sections, clusters them, develops performance prediction models, and identifies maintenance treatments to create a decision support tool. Distresses data collected over 6 years, pre and post-monsoon, were used to calculate pavement condition indices. Sections were grouped using the K-means algorithm for better modeling, and a deterministic deterioration model estimated pavement conditions over time. Optimized maintenance treatments for 5 years were determined using Generic Algebraic Modeling System software. A user-friendly geographic information system interface graphically represents the road network, incorporating collected data and optimized treatments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".