Detection of Delamination in the HMA Layer of Runway Pavement Structure Using Asphalt Strain Gauges
Bibliographic record
Abstract
Asphalt pavement distresses like surface shoving and slippage cracking can be found at airports in areas where aircraft brake and turn, such as high-speed exits, as a result of the high surface-shear forces. Slippage failure is typically caused by the deterioration of bonding between asphalt layers (delamination), or a lack of shear resistivity within the surface-layer asphalt mix. High pavement temperatures have also been shown to contribute to slippage failures in asphalt concrete pavements. At the intersection of Runway 4 R-22 L and High-Speed Taxiway N (HST-N) at Newark Liberty International Airport (EWR), interlayer delamination was determined to be the cause of shoving and slippage cracking on the pavement surface. In 2012, asphalt strain gauges were installed during a scheduled repaving of the runway and taxiway. This paper details the components of an asphalt strain gauge instrumentation system, and analyzes the strain responses collected from the gauges installed at EWR. By identifying large discrepancies in strain responses between strain gauges installed in the hot mix asphalt (HMA) overlay and lower layers of asphalt pavement (HMA milled surface), areas of potential delamination were identified. Delamination was successfully detected by the instrumentation and is shown to increase in severity over time, especially in the gauges nearest to the taxiway lead-line (centerline). Strain responses are also affected by temperature at the interface and aircraft speed. Photographs of the taxiway surface taken in the summer of 2014 confirm that slippage distress is occurring in the same areas in which the strain gauges indicate delamination. Statistical analysis methods were employed, and the Difference between Means tests and Kolmogorov-Smirnov Tests confirm that the measurement of Δ strain (Δε) was significantly distinct for bonded versus delaminated pavements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".