Rapid concrete surface roughness assessment with smartphone LiDAR
Bibliographic record
Abstract
Concrete surface roughness is integral to interface shear resistance and overall integrity between concrete cast at different times, influencing structure safety. This interface is found at common construction joints that enable composite action between precast and cast-in-place concrete elements or monolith behaviour between two cast-in-place pours. Traditional on-site roughness evaluation relies on qualitative methods, such as visual comparison with predefined surface profiles. These assessment methods are subjective, time-consuming, and inconsistent. The absence of quantitative methods creates a notable gap in the data-to-decision framework. Recent advancements in smartphone LiDAR technology hold potential to provide a solution. Our study introduces a novel method that leverages smartphone LiDAR technology to precisely measure concrete roughness quantitatively. Data is acquired from the LiDAR system in the form of a point cloud, which captures the three-dimensional (3D) structure of the surface. Our study comprises comprehensive laboratory experiments to assess LiDAR operability, followed by on-site experiments for roughness quantification across five sites with varying levels of concrete roughness. The LiDAR data are compared with ground truth 3D data collected using a structured light sensor. The results demonstrate that the proposed method presents a reliable alternative for measuring concrete surface roughness in the field.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".