Determining Return on Long-Life Pavement Investments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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