Sustainable Infrastructure Is a Two-Way Street: Balancing Environmental and Condition Performance Goals
Bibliographic record
Abstract
Transport agencies are under increasing pressure to mitigate the global warming impact of our pavement systems. This objective, however, must be carefully balanced with other performance metrics of interest (e.g., pavement condition) for federal and state agencies. This paper details a network-level tool aimed at supporting transport agencies in achieving two competing objectives: (1) maximizing the number of pavement segments in a good state-of-repair; and (2) minimizing the global warming impact of the network. The stochastic optimization model follows a two-stage bottom-up approach, where optimal policies are learned for individual facilities, and those decision-rules are subsequently used to guide the allocation of resources across the network. The model is applied to a realistic roadway network composed of 159 miles of pavement segments based on data made available via the Highway Performance Monitoring System. The case study results highlight that, over a 20-year analysis period, maximizing the condition of pavement assets across the network increases its expected global warming impact by 1% to 8%. The results also highlight that, invariant to the selected objective and/or available budget, increasing the allocation of funds toward rehabilitation activities rather than reconstruction treatments improves the overall condition of the network by as much as 25% and reduces its global warming impact by up to 7%. The results of the case study provide decision-makers with important insights around the impact of pavement management performance goals and budgetary policies on the global warming impact of pavement systems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".