Life Cycle Cost Analysis in pavement type selection
Bibliographic record
Abstract
This report summarizes the findings from a research investigation conducted to evaluate life cycle cost analysis (LCCA) practices among state highway agencies for pavement type selection process, and proposes a probabilistic LCCA approach for use in South Carolina. This investigation was based on analysis of data obtained from a preliminary and a final survey of states across the U.S. and provinces across Canada. The surveys were designed to gauge the level of LCCA activity in different states as well as to solicit information on specific approaches that each state is taking for pavement type selection. The responses obtained from the surveys were analyzed to observe the trends and ranges of various input parameters that feed into the LCCA process. Based on the data from surveys, selected states whose LCCA practices exemplified a progressive a comprehensive approach were identified and further questioned on specific aspects of their respective LCCA approaches. Based on this analysis, a probabilistic-based LCCA approach is proposed for use with pavement-type selection process in South Carolina. Also, specific recommendations on range of values for different input parameters based on the survey data are made. Where no adequate database exists for certain LCCA input parameters, suggestions are offered for developing a database of values for future use. In addition to developing a protocol for a probabilistic LCCA approach, different LCCA software such as REALCOST, DARWin and other customized software used by specific states were explored. Amongst these, REALCOST software developed by Federal Highway Administration (FHWA) was found to be widely used by several state agencies and most comprehensive in its treatment of different input parameters. Further, FHWA has been instrumental in providing support to customize the REALCOST software to meet individual state's needs. Based on these findings, REALCOST software was proposed as preferred software for use with conducting LCCA for pavement-type selection.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".