Evaluation of the State-of-the-Art Automated Construction Progress Monitoring and Control Systems
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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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".