A life-cycle approach for managing road infrastructures in developing countries based on Asset Management
Bibliographic record
Abstract
Road infrastructures are very important to economic activity, especially in developing countries where they play an essential role in marketing agricultural products and providing access to health, education, and other services. While economic growth and the investments in road transport have increased heavily in developing countries, the public sector responsible for their life-cycle planning, management and maintenance is struggling to make the necessary reforms to keep up with the pace. The main objective of this dissertation is to understand the patterns that influence the strategic planning of road infrastructures and the successful implementation of the practices of asset management in the regulatory environment and structure of the responsible authorities in the developing countries. These patterns (external drivers), different in each country, if not researched and understood correctly, may affect the outcome of the results for the upcoming decades and jeopardize the entire implementation of asset management processes within the organizational structures of the developing countries. It reviews and analyzes the National regulatory environment and practices in Top Asset management countries (Canada, Australia, New Zealand, Uk, USA) and current social and political situation in the western Balkans region (developing countries region), which is influencing the successful management of primary infrastructures in this region. A significant Case study from Albania (Highway Durres- Kukes - Morine, a segment of European route 7 between Albania and Serbia), is introduced and actual physical Conditions, value, and performance of the highway are taken in consideration. Description of Problems this highway experiences because of lack of life-cycle planning and management are presented and how the mismanagement of the assets on a strategic level leads to tangible problems on the technical level. Transport impacts on the highway in terms of displacement, traffic flows, and forecast, historical traffic data are analyzed in order to analyze capacity/demand patterns and future demand, the influence it has on Road asset management and relate this with different strategies of maintenance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".