A multi-objective approach for the management of aging critical highway bridges
Bibliographic record
Abstract
This paper presents an approach for a multi-objective-based management of aging critical highway bridges to improve their life cycle performance with emphasis on improving public safety and security. The proposed multi-objective optimization framework is presented as an effective approach that overcomes some of the limitations and complexities of cost-benefit or risk analyses, where all consequences need to be expressed in monetary terms. The framework prioritizes first the critical bridges and then identifies the risk mitigation measures that can be used to satisfy several possible management objectives such as maximizing public safety and public security, minimizing traffic disruption and minimizing costs. Risk mitigation strategies can include more frequent and/or more in-depth inspections, load rating, monitoring of the structural performance and security of bridges, rehabilitation and strengthening of damaged elements, and protection of weak and vulnerable components against extreme shocks due to natural hazards or intentional attacks. A multi-objective criticality index is proposed as a prioritization criterion that achieves an adequate best trade-off between all identified and conflicting objectives. The implementation of the proposed approach is demonstrated on three examples that illustrate the prioritization process on a hypothetical network of ten critical bridges and the use of different risk mitigation measures, such as health monitoring and deterioration prediction models on bridge structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".