Ability of Technologies to Collect the Positioning and Occupancy of Work Crews and Resources on Construction Job Sites
Bibliographic record
Abstract
Introduction Efficiently updating construction schedules is vital for managing dynamic workflows. Traditional methods like Gantt charts rely on manual updates, but newer approaches, such as Chronographic Modelling and Dynamic Modelling of Occupancy Rate Scheduling (DMORS), require tracking spatial positioning and occupancy rates of teams and resources. Given the complexity of these tasks, emerging technologies are being adopted to enhance data collection. Methods This study reviews recent advancements in tracking construction site occupancy and spatial positioning. It categorizes these technologies into three main types: image and video capture, 3D point cloud generation, and sensor-based tracking systems. Each method is evaluated based on seven essential criteria: data collection speed, portability, accuracy, worksite impact, post-processing time, accessibility, and technology maturity. Results The research provides a selection framework for contractors, helping them choose the most suitable tools based on project size and complexity. High-precision tools like LiDAR and laser scanning are ideal for large contractors requiring detailed modelling, while smaller firms may prefer cost-effective solutions such as manual data collection with photos or videos. Discussion This research highlights that no single technology meets all needs for tracking construction site occupancy. High-precision tools offer accuracy but may disrupt work, while simpler methods are easier to use but less detailed. So, depending on the size of the contractor or the project, the best technology may vary. Conclusion Ultimately, this paper supports the integration of space planning schedules in construction management by offering a structured approach to implementing modern data capture technologies. The study also highlights future trends, advocating for multi-technology integration to improve accuracy and exploring the potential of artificial intelligence for automated data analysis.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".