Tile-Based Pavement Rutting and Roughness Evaluation Using Mobile LiDAR Data
Bibliographic record
Abstract
Pavement distresses, including rutting and roughness, have impacts on both driver comfort and safety. Therefore, timely identification of such distresses is essential. Traditional inspection methods are laborious and time-consuming while existing automated detection systems have primarily focused on crack detection. This paper introduces a novel tile-based method that utilizes light detection and ranging (LiDAR) technology to automatically quantify pavement rutting and roughness. Computational geometry and surface fitting techniques are applied to generate the road surface model with and without deformations to identify rutting in pavement sections. LiDAR-generated profiles were automatically generated and used to assess roughness using the quarter-car model. The approach was tested on five road sections in Alberta. The results show accurate measurements of shallow rut depths at 3 mm and higher, along with distress spatial information. The roughness estimation results demonstrate the ability to capture vertical variations along road profiles at fine resolutions, consistent with rideability analysis software. The runtime for rutting estimation is approximately 83 s/k m and 47 s/km for IRI computation. The results highlight the feasibility of efficiently extracting distress information from point cloud data for organizations aiming to leverage mobile LiDAR data sets in pavement evaluation applications.
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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 | 0.001 |
| 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 | 0.001 | 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".