Improving Airport Pavement Management Using An Analytical Hierarchy Process Decision Making Tool
Bibliographic record
Abstract
This paper discusses how an Airport Pavement Management System (APMS) can be used by airport operators to help improve maintenance scheduling and decision making. The steps involved in creating an APMS are outlined from establishing a pavement history to maintaining the system with current information. Opportunities are identified for utilizing the APMS to analyze trends in pavement distresses and evaluate the effectiveness of competing maintenance treatments. This paper also introduces an Analytical Hierarchy Process (AHP) as a tool that can be incorporated in an APMS and utilized for decision making. An AHP offers a systematic approach to incorporating both qualitative and quantitative factors in the assessment of competing alternatives to provide an innovative solution. A runway surface texture and rubber removal case study is presented. In this case study, it is shown that the state of the art practice testing frequency can be greatly improved with access to data. The importance of runway friction is described and four options for removing rubber accumulation and restoring texture to a runway are presented to provide context for applying this case to an AHP. The paper concludes by showing how an AHP can be incorporated into an APMS to help an operator compare maintenance techniques and select the most suitable alternative based on their airport's needs. The concept of an AHP can be broadly applied to decision making within an APMS.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".