Analysis of Runway Pavement Distress Using Embedded Instrumentation
Bibliographic record
Abstract
Airport pavements are constantly impacted by the heavy braking, and turning of aircraft that are major contributors to pavement failures such as surface shoving and slippage cracking; pavements are also greatly affected by high ambient and in-pavement temperatures during summer months. To detect airport pavement failures, the Federal Aviation Authority (FAA) implemented and installed strain gages in pavements at a few select major airports. This research focuses on results analyzed from strain gages installed by FAA that show pavement failures at the intersection of runway 4R-22L and High-Speed Taxiway N (HST-N) at Newark International Airport (EWR). Data from the pavement was collected by a data acquisition cabinet and transferred to a database for data analysis. The strain gage readings are described in technical terms, and the physical separation over time between the asphalt base layer and upper repaved layer is demonstrated. It is seen that non-destructive testing using embedded sensors can give warnings of pavement distresses that ultimately lead to failure. Statistical analysis using the Kolmogorov-Smirnov test and the differences between means test further confirm the pavement failures detected by the strain responses at EWR. The progression of pavement distress is described and evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".