Risk-based life-cycle analysis of highway bridge networks under budget constraints
Bibliographic record
Abstract
This paper describes the life-cycle analysis of the risk to highway bridge networks subjected to overweight traffic loads under maintenance budget constraints. The risk to the network is initially evaluated based on the current condition of its constituent bridges and then the life-cycle analysis is performed to identify the level of risk reduction that is possible given a particular budget allocation. The deterioration rate is extracted from National Bridge Inventory data. The risk is monitored as the deterioration of bridges progresses over the years. Bridges are ranked in descending order starting from the bridge whose failure has the highest impact on network risk. Maintenance is scheduled sequentially on the riskiest bridges until the entire budget allocation is depleted. The methodology is illustrated using as an example the highway network of major interstate and state roads in New York State. The paper compares the results of the proposed life-cycle risk analysis for several budget levels. The results show that network risk can be reduced from its present level by 20% if current expenditure rates are maintained. Risk reduction can reach 37% if the budget for bridge rehabilitation is increased to match that recommended in the ASCE report card for America’s infrastructure.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".