Road Damage Identification Using a Combination of UAV Quadcopter Technology and Subgrade Investigation
Bibliographic record
Abstract
Good road infrastructure is essential for traffic safety.However, more than 60% of roads in developing countries are in poor condition.As a developing country, Indonesia has 540,000 kilometers of roads, of which 40% are still in poor condition.Fast identification is needed to handle road damage.This research aims to identify road damage quickly using Unmanned Aerial Vehicle (UAV) and subgrade investigation through Dynamic Cone Penetrometer (DCP) testing.This study employs an experimental method, where the results of UAV aerial imagery are digitally processed using Agisoft Metashape and ArcGIS software with polygon analysis.Subgrade testing was conducted using DCP to measure the California Bearing Ratio (CBR) value.A descriptive study analyzed the relationship between subgrade conditions and road damage affecting traffic safety.The results showed 114 damage points with five types of road damage with a total area of 1,564.93 m 2 .UAV mapping accuracy using omission and commission tests reached 98.39%, indicating highly accurate data.The average subgrade CBR value only reached 1.44%, indicating very poor soil conditions.This condition contributes to road damage, including potholes, depression, and cracks.Road repairs and subgrade improvements are needed to prevent further damage and improve road safety.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".