Guidelines for Probabilistic Pavement Life Cycle Cost Analysis
Why this work is in the frame
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Bibliographic record
Abstract
To select the most appropriate pavement design for a given situation, it is necessary to understand how the pavement properties and in-service conditions relate to performance and life cycle cost. A given design may be most appropriate on one type of road and least appropriate on another type of road. This design selection is further complicated by the advent of new design methodologies, materials, and construction delivery techniques. Life cycle economic analysis is an important tool for comparing alternative treatment strategies. A life cycle analysis can use a deterministic approach, which incorporates a single point value, or it can use a probabilistic approach, which includes a mean, variance, and probability distribution. The probabilistic approach is better suited to describing the uncertainty associated with engineering. The Canadian Strategic Highway Research Program Canadian Long-Term Pavement Performance database and data provided by the Ministry of Transportation of Ontario were used in this analysis. Most construction variables are generally believed to be best described by a normal distribution. However, a lognormal probability distribution is better suited to describing these variables. This best fit is based on both a mathematical examination and a comparison of similar variables such as stocks and real estate values. It is also shown that thickness is a probabilistic variable that should be combined with the cost and incorporated into pavement life cycle costing. Ignoring the lognormal nature of these variables introduces bias into a life cycle cost analysis and does not reflect the true overall cost.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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