Applicability of 3D laser scanning and close-range photogrammetry for geotechnical laboratory tests
Bibliographic record
Abstract
Abstract Non-contact surveying strategies such as terrestrial laser scanning (TLS) and digital close-range photogrammetry (DCRP), have recently become popular as surveying techniques due to their rapid deployment and high accuracy. The critical issue that researchers typically face is the limited number of LVDTs or dial gauges available in laboratories, and occasionally it might be physically challenging to install several gauges in the testing facilities. Consequently, the primary objective of this paper is to examine the present viability and benefits of employing TLS and DCRP techniques in monitoring geotechnical applications. Calibration of these methods was performed through two laboratory tests: (1) monitoring of secant pile walls (SPW) as well as the soil movements; and (2) axial compression tests on SPW. The findings reveal that the discrepancy between traditional measurement methods and the TLS approach is less than 3.0%, whereas the difference between traditional methods and DCRP is under 1.8%. Furthermore, both DCRP and TLS techniques are capable of precisely tracking initial deformations, geometric irregularities, deficiencies in the samples (including pre-buckling phenomena), and deformations of the soil tank at each stage of loading. In conclusion, TLS and DCRP methods were found to offer accurate and advantageous alternatives for geotechnical monitoring, notably their capacity for the automatic collection and analysis of an unlimited number of measurement points.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".