A review of multi-criteria decision-making methods for infrastructure management
Bibliographic record
Abstract
In infrastructure management, multi-criteria decision-making (MCDM) has emerged as a decision support tool to integrate various technical information and stakeholder values. Different MCDM techniques and tools have been developed. This paper presents a comprehensive review on the application of MCDM literature in the field of infrastructure management. Approximately 300 published papers were identified that report MCDM applications in the field of infrastructure management during 1980–2012. The reviewed papers are classified into application to the type of infrastructure (e.g. bridges and pipes), and prevalent decision or intervention (e.g. repair and rehabilitate). In addition, the papers were also classified according to MCDM methods used in the analysis. The paper provides taxonomy of those articles and identifies trends and new developments in MCDM methods. The results suggest that there is a significant growth in MCDM applications in infrastructure management applications of MCDM over the last decade. It has also been noted that many decision support tools based on multiple MCDM methods have been successfully used for infrastructure management.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".