Bibliographic record
Abstract
Life Cycle Cost Analysis (LCCA) is performed by transportation agencies in the design phase of transportation projects in order to be able to implement more economical strategies, to support decision processes in pavement type selection (flexible or rigid) and also to assess the relative costs of different rehabilitation options within each type of pavement. However, most of the input parameters are inherently uncertain. In order to implement the LCCA process in a reliable and trustworthy manner, this uncertainty must be addressed. This thesis summarizes a through research that aims at improving the existing LCCA approach for South Carolina Department of Transportation (SCDOT) by developing a better understanding of the parameters used in the analysis. In order to achieve this, a comprehensive literature review was first conducted to collect information from various academic and industrial sources. After that, two surveys were conducted to survey the state-of-the-practice of LCCA across the 50 U.S. Departments of Transportation (DOTs) and Canada. The questionnaires 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 web surveys were analyzed to observe the trends regarding the various input parameters that feed into the LCCA process. The results were combined with the additional resources in order to analyze the challenges to implementing the LCCA approach. The survey results showed LCCA is used widely among transportation agencies. However, the extent of the analysis varies widely and is presented here.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".