Determining Return on Long-Life Pavement Investments
Bibliographic record
Abstract
It is becoming increasingly necessary in life-cycle analysis (LCA) of infrastructure assets, including pavements, to take a longer-term approach than has been used, mainly to ensure sustainability and assess the impacts of today's decisions accurately. LCA can include primarily life-cycle cost analysis (LCCA), but it also can include considerations of resource conservation, environmental impacts, energy balance, and so forth, and it can involve short-, medium-, and long-term periods. It is thus possible to develop a context for LCA of likely and uncertain societal activities, including transportation, over these periods. Conventional LCCA is directed toward comparing competing alternative investment strategies and can involve a range of stakeholders. Of the methods available, present worth of costs is almost exclusively used in the pavement field. However, when medium- to longer-term life-cycle periods are involved, rate-of-return and cost-effectiveness formulations can be applicable. A numerical example shows how an agency can determine the internal rate of return for two investment alternatives involving different pavement designs and a life-cycle period of 50 years. In addition, a cost-effectiveness example is provided for a sidewalk network, again with a life-cycle period of 50 years. Conventional LCCA for calculating present worth of costs will undoubtedly continue to be used in the pavement field as a primary tool. However, using a rate-of-return or cost-effectiveness formulation, especially for medium- to longer-term life-cycle periods, should be given more consideration.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".