A Comparison of TLS-based and ALS-based Techniques for Concrete Floor Waviness Assessment
Bibliographic record
Abstract
A Comparison of TLS-based and ALS-based Techniques for Concrete Floor Waviness Assessment Nisha Puri and Yelda Turkan Pages 1142-1148 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Laser scanning-based techniques have been applied for checking the dimensional tolerances of concrete elements. Several studies utilized Terrestrial Laser Scanning (TLS) for measuring concrete floor waviness. The results of those efforts have shown that accurate floor waviness information can be obtained using TLS. Unmanned Aerial Vehicles (UAVs) mounted with cameras and 3D laser scanning sensors, referred to as Airborne Laser Scanning (ALS) hereafter, have versatile applications in construction, such as surveying, progress control, 3D modelling and inspections. As-built data collection for dimensional quality assessment can be a potential application of such technology. In particular, the application of ALS for assessing the waviness of concrete slabs warrants further exploration. This study presents the results of a comparative analysis of floor waviness measurement results obtained using ALS and TLS-based technologies. Continuous Wavelet Transform (CWT) is applied to the depth map derived from both point cloud datasets to obtain waviness information. Comparable results are obtained for the CWT scales of 30, 60 and 75. Detailed discussions on how the results can be improved are presented. The analysis of the accuracy of results obtained using ALS advances its application in the field of dimensional quality assessment. Keywords: TLS; ALS; Continuous wavelet transform; Depth map; Point cloud; Dimensional quality control; tolerance compliance DOI: https://doi.org/10.22260/ISARC2019/0152 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".