Use of Advanced Technologies for Topographic Surveys in Civil Construction
Bibliographic record
Abstract
The complexity that has characterized market relations in recent years, with demands for product and process innovation, has even had repercussions on more traditional activities, such as the civil construction segment. Behind only agriculture, the construction industry represents 13% of the global GDP and its volume is US$ 8 trillion per year. However, construction projects often exceed budget by 80%, and deadlines by 20 months. Between 8% and 10% of productivity gains are related to the insertion of technologies (IPEA, 2022). In the field of civil construction, these digital technologies, such as cloud computing, automation, virtual reality and augmented reality, 3D modeling, communication applications and BIM—Building Information Modeling and even machine learning, have been called Construction 4.0. This article evaluates the feasibility of replacing the use of Terrestrial Laser Scanner (TLS) by the use of Unmanned Aerial Vehicles (UAV). For this, it presents a comparison in the use of equipment for carrying out planimetric surveys in civil construction, using as an example the UAV and the TLS—more modern equipment, in addition to the total station—more conventional equipment—for surveying control points. The results show that in the UAV image processing, the RMSE presented a centimeter accuracy (1.93044 cm) for the model. Even if the accuracy of the models generated by TLS is millimetric, it can be considered that the results obtained here were satisfactory, however it is necessary to apply imaging techniques more efficiently to obtain a more accurate product, in order to arrive at millimeter accuracy. Studies on better positioning of targets and georeferencing of models would also be of great contribution to applications in civil construction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".