Enriching Existing 3D Pavement Condition Survey Datasets to Support Paving Project Cost Estimates, Project Planning, Designs, and Automated Machine Guidance
Bibliographic record
Abstract
State departments of transportation (DOTs) typically perform annual pavement condition inspections, which serve as an important input into pavement management systems (PMS) software. Road surface defects (cracking, rutting, smoothness, etc.) are analyzed by PMS software to model the deterioration of pavements and to make budget and performance-based recommendations about which roads to maintain and how and when to maintain them. Increasingly at the state DOT level, these data are captured using high-speed 3D lasers (laser triangulation systems) that acquire the 3D shape of the road surface to evaluate its condition. Traditionally the capture of road elevation data relied entirely on the use of survey crews. Although accuracy can be quite high, the process of capturing elevations can require a lot of manpower, is time-consuming, requires lane closures, and results in a relatively small number of points per kilometer of road with which to perform all of the tasks from early project planning through construction. This paper explores an alternate approach that leverages existing 3D laser technology utilized by DOTs to measure the condition of in-service pavements. Typically, these laser systems capture “relatively referenced” 3D profiles of the roadway to evaluate pavement condition based on surface distortion. However, there is often no connection between these “relative” 3D profiles and real-world locations. This new approach involves the addition of high-accuracy blended global navigation satellite system + inertial navigation system positioning systems, as well as specialized software, to map the absolute position of 3D profiles in real-world coordinates.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".