A multi‐attribute ranking method for bridge management
Bibliographic record
Abstract
Purpose A bridge network is a major capital asset that requires continuing investment in order to maintain the network within acceptable limits of safety and serviceability. Ranking and prioritizing procedures have been widely used by several departments of transportation to select bridges for intervention and to distribute the available funds among competing projects. The available ranking and prioritizing procedures have various drawbacks, and an improved, rational ranking and prioritizing procedure is needed. The paper aims to address these issues. Design/methodology/approach The requirements and characteristics of an innovative ranking and prioritizing method are identified during interviews with professionals involved in bridge management. Based on these requirements, multi‐attribute utility theory (MAUT) is selected to develop the method. A technique to develop utility functions based on the analytical hierarchy process (AHP) is discussed. A hierarchy structure that captures the decision‐making elements is presented. A case study is used to demonstrate the applicability and the validity of the proposed ranking method. Findings The research findings have identified the decision objectives and the criteria essential to rank and prioritize bridge projects, and these are included within a framework to rank and prioritize bridge projects while incorporating experts' input in the process. Practical implications The proposed framework includes weights for the various objectives and recommends utility functions to evaluate the different attributes. In addition, the framework provides flexibility to adjust the weights and to modify the utility functions to reflect network‐specific characteristics. This method can be used by departments of transportation to rank bridges in a network, even incorporating conflicting criteria, and it can be integrated within an already implemented bridge management methodology. Originality/value Ranking and prioritizing projects are essential steps in bridge management. Current methods for ranking and prioritizing bridge projects are associated with various drawbacks. This paper proposes an innovative ranking method for bridge networks, based on MAUT. This theory provides flexibility for the decision makers in expressing their degree of satisfaction with each bridge attribute.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".