Analytic Hierarchy Process as a Tool for Infrastructure Management
Bibliographic record
Abstract
The role of infrastructure management has been continuously changing since the late 1980s. Public agencies have started to incorporate private-sector practices. These new practices include the use of customer inputs to develop new goals and policies, development of new evaluation procedures for priority programming optimization, and addition of feedback loops into infrastructure management systems. One of the new evaluation procedures adopted into infrastructure management is the analytic hierarchy process (AHP). AHP is a decision-making tool that incorporates both qualitative and quantitative factors. AHP has increased in use and popularity because of its ability to reflect the way people think and make decisions by simplifying a complex decision into a series of one-on-one comparisons. The results are then synthesized and presented as a percentage of all the options evaluated. This presentation will illustrate AHP with two examples. In the first example, AHP was used to compare fast-track concrete repair products on the basis of the priorities set by a public agency. Three fast-track concrete repair products were compared with the use of 16 criteria comprised of construction procedures and physical properties. Without field testing, AHP showed that two of the tested products were superior to the other. In the second example, AHP was used to compare seven maintenance, rehabilitation, and reconstruction strategies for asphalt pavements. This comparison was based on nine priorities compiled from a survey of road users.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".