A New Approach for Structural Monitoring Based on Terrestrial Laser Scan Data Using Control Planes
Bibliographic record
Abstract
Laser scanning is a survey method that enables the obtaining of several data points on surfaces through the observation of horizontal and vertical angles and electronic distance measurements. The result of these observations is a set of 3D points named point clouds, which can be obtained from scans using total stations or the Terrestrial Laser Scanner (TLS). Currently, the use of TLS in geodetic structural deformation monitoring activities is under evaluation, since this technique can provide a greater number of points in a shorter period, when compared to scans performed by total stations. In this research, the feasibility of using control planes for geodetic monitoring was investigated in a controlled laboratory environment, exploring the current trend of parameterization of point clouds. From the development of an Experimental Plane Control (EPC) containing a flat surface that could be inclined in a controlled way, simultaneous scans with TLS and total station were carried out at different inclinations of the control plane. Both surveys were done in frontal scan mode (minimum plane inclination of 0º53’ and maximum plane inclination of 3º28’) and in oblique mode (plane inclination of 1º09’), where the plane inclination angles were generated by the rotation in EPC base, considering the first plane position as reference to inclination calculating. From parametrization of point clouds, a new approach was developed to test the significance of the estimated inclination angle of the plane in relation to a reference position. The results showed that it is possible to confirm that the inclined angles were statistically significant, but that the TLS scanning position can interfere in the determination of these data for monitoring purposes.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".