Strategic Total Highway Asset Management Integration
Bibliographic record
Abstract
To manage a significant quantity of aging roadway infrastructure and growing traffic volume successfully, agencies are faced with challenges in developing reliable long-term plans that maximize network performance by optimizing programming preservation projects at the network level. Current practice typically involves relatively independent planning for bridge and pavement subassets, with a slight number of situations allowing for reliable trade-off analysis between the two. The choice to improve two bridges rather than one pavement section may yield a greater percentage increase in the bridge network performance than one pavement section would for the pavement network performance. The reliability of this choice being right and at the right time significantly decreases over the long term. Mutually inclusive highway asset planning by an integration of the bridge subasset into pavement subasset significantly increases long-term planning reliability. A key point of this strategic total highway asset management integration (STHAMi) approach is the conceptual structural integration factor. Integrating the bridge condition index into a pavement performance index allows for the treatment of bridges as equivalent pavement sections. STHAMi resulted in a higher percentage of model network treated per unit of value, coupled with consistently higher annual network performance during a 25-year span. Key benefits include the introduction of one pavement performance indicator as an overall encompassing highway performance measure for combined long-term bridge and pavement subasset planning. The approach makes long-term planning for both subassets possible in a pavement-oriented engineering organizational unit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".