Evaluation of the State-of-the-Art Automated Construction Progress Monitoring and Control Systems
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
Efficient onsite data acquisition of a construction project enables the comparison of the actual state of the project to the as-plan state so that potential delays can be identified early within the project life cycle. Traditionally, onsite data are collected manually, a time consuming, costly and error-prone task, and therefore not justifiable in modern construction management. To overcome the challenges corresponding to such manual approaches, the application of automated progress monitoring of construction sites has attracted the attention of researchers. To enable an effective application, it is necessary to evaluate the reliability of the available technologies in collecting onsite data. In this paper, a qualitative evaluation of the applicability of the state-of-the-art automated progress monitoring technologies, namely camera, LiDAR, and 3D range imaging, has been carried out. A set of experiments has been carried out to compare the time of data collection for each technology. LiDAR provides the most accurate 3D estimates. The time of data collection of the Leica HDS6100 laser scanner is shown to be seven times faster than that of the DSLR camera in an indoor construction site simulated laboratory. However, the cost of LiDAR devices is the major economical drawback of the technology.
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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 | 0.005 | 0.001 |
| 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 | 0.002 |
| 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