Development and implementation of an Airfield Pavement Management System for the Winnipeg International Airport
Bibliographic record
Abstract
A Pavement Management System (PMS) is a tool that aids in determining the most effective application of maintenance and reconstruction (M&R) work for a given pavement network. The purpose of this research is the development and implementation of an Airfield Pavement Management System for the Winnipeg International Airport (WIA) that expands on the capabilities and usefulness of conventional systems. The system developed in this research utilizes geo-referenced pavement distress data collected using a GPS receiver. Time required to complete Pavement Condition Index surveys has been reduced, minimizing the impact on airport operations of conducting an airfield inspection. Using geo-referenced distress data leads to a multitude of new analysis techniques that allow for optimum management of the pavement network. Several pavement deterioration models have been created using the collected pavement condition data. These models aim to predict pavement condition at points in the future to aid in M&R planning. Models were created using the least-squares regression technique as well as neural network modelling. The use of neural networks appears promising as they are not constrained to a single regression parameter and can account for the interaction b etween parameters and nonlinear relationships. The WIA PMS represents a significant improvement to the functionality of current PMSs by expanding the analysis and modelling capabilities while reducing the effort associated with data collection.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".