Incorporating Variability into Pavement Performance Models and Life Cycle Cost Analysis for Performance-Based Specification Pay Factors
Bibliographic record
Abstract
This paper describes a recent research study that examined how changes in design life impacts the pavement life cycle cost (LCC) and ultimately how the reduction or addition in LCC attributed to inferior or superior in-service performance could be used as a basis for establishing a pay factor for a performance based specification. Models have been developed using data from the Canadian Long Term Pavement Performance (C-LTPP) that indicate that overlay thickness, total prior cracking, annual freeze index, annual days with precipitation, and accumulated ESALs after eight years, affect the slope of pavement deterioration for asphalt overlay pavements. One of these models, as well as data from the United States Long Term Pavement Performance (LTPP) test sites, is used to determine the service life of asphalt overlay pavements. This paper examines how the variability associated with overlay thickness, total prior cracking, and accumulated ESALs after eight years affects the service life. Furthermore, this paper considers the variability associated with the discount rate and incorporates all associated variability into the life cycle cost analysis (LCCA). The life cycle cost distributions are
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".