Pavement Design Issues and Embankment Construction for the Second Runway at Cancn International Airport, Mexico
Bibliographic record
Abstract
Cancún International Airport (CUN) located on the tip of the Yucatán peninsula in southeast Mexico is the second largest airport in Mexico. It served more than 11.3 million passengers in 2007 - the most international passengers in Latin America and Cancún is the biggest tourist destination in Mexico and the Caribbean. Aeropuertos del Sureste (ASUR) group has a concession contract for 9 airports in the southeast of Mexico and under their Master Development Agreement are committed to the construction of a second (parallel) runway at Cancún International Airport by the end of 2009 in order to meet the ever increasing demand for air services at this key worldwide tourist destination. This paper describes the key pavement geotechnical issues, design methods and construction materials and techniques utilized to engineer and build the embankment for the new 2800 metre (9200 foot) runway. The main design and construction issues covered include: discussion of the greenfield site featuring variable karst topography with the potential for extensive surface and hidden subterranean cavities; pavement design criteria including aircraft loading and minimum elevation for hurricane flooding; high water table and drainage by infiltration; design of runway and taxiway stabilized flexible pavements to FAA criteria including utilization of FAARFIELD methodologies; development of heavyweight proofrolling methods to confirm suitability of natural subgrade and identification of cavities; and, utilization of local materials and construction methods for construction of the subgrade embankment and pavement structure layers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".