New Insights Into Road Cavity Detection From GPR Data
Bibliographic record
Abstract
Maintaining the integrity of transportation infrastructure is critical for resilience and safety.Subsurface changes, along with climate change and aging infrastructure, can all contribute to the development of sinkholes, a critical concern for infrastructure.However, early detection is possible through characterization of the factors that influence sinkhole formation.Ground-penetrating radar (GPR) is a practical tool for non-destructive subsurface monitoring and early detection of sinkholes.Nevertheless, conventional GPR evaluation relies heavily on subjective analysis.Deep learning (DL) techniques can automate and improve GPR data analysis, especially for large amounts of collected data.Despite the success of DL in the field of computer vision, limited data availability prevents its widespread application in GPR surveys.In this paper, an overview of GPR applications for cavity detection in transportation infrastructure is discussed, highlighting key findings and limitations.It also explores data preparation techniques, including synthetic data generation and data augmentation, to facilitate the automation of cavity detection from GPR data using DL approaches.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".