Alternative to AHP approach to criteria weight estimation in highway bridge management
Bibliographic record
Abstract
Criteria weights formulation is the most essential component of a multi-criteria prioritization scheme. The research reported in this paper sought to critically compare a recently developed multi-criteria optimization criteria weights method to the well-known analytic hierarchy process (AHP) as well as the method employed by the bridge management software BrM (formerly Pontis), which is licensed to all 50 State Departments of Transportation in the United States. Using four criteria for demonstration (bridge performance, utility, vulnerability to climate-triggered extreme events, and vulnerability to climate-triggered extreme loads), the new method clearly separates the concept of prioritization from the concept of optimization. The study demonstrates that there is an important and practical difference between a suitable criteria weight formulation for asset management optimization, on one hand, and the type of formulation required for a general selection problem, on the other hand. More importantly, the new method performs better than both the AHP and the BrM on all the important measures.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".