Flexible Pavement Life Cycle Cost Analysis by Using Monte-Carlo Method and the Suggestions for Developing Countries
Bibliographic record
Abstract
Flexible Roadpavement plays an essential role in developing an effective, economic, and safe operation road network of any country. In Vietnam, a developing country, the selection of a suitable flexible pavement structure is always a challenge due to fiscal limitations. The traditional determinant method (TDM) by which pavement structures are selected mainly on the basis of initial construction costs and traffic load has been used for many years in the nation. This paper presents the use of Monte Carlo simulation to analyzethe entire flexible pavement life cycle cost. Data including initial and maintenance costs and road user costs were collected from several different types of existing flexible pavement in Nghe An province, Vietnam. Random variations of several main inputs were explored in order to develop density distribution functions. These functions then were used as the bases for Monte Carlo simulation. One million simulation runs were implemented and the Net Present Values (NPVs) among pavement types were compared under the light of risk analysis. Research results showed that TDM method provided bias and uncertain results compared to that of Monte Carlo one. In terms of long-term pavement performance, a low-cost pavement structure should not always be considered as a wise selection. Some other suggestions for a developing country as Vietnam were also included.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".