Rapid concrete surface roughness assessment with smartphone LiDAR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it