Integrating Edge Computing and Cloud BIM for Enhanced Real-Time Safety Monitoring in Construction Sites: Reducing Time-Latency and Improving Data Accessibility
Bibliographic record
Abstract
Purpose This study aims to reduce time latency and improve data integration in Proximity Warning Systems (PWS) used for construction safety monitoring. Traditional systems often suffer from delays that compromise worker safety, highlighted the need for a more responsive and integrated approach. Methodology This research proposes a hybrid PWS architecture that integrates edge computing with Industry Foundation Classes (IFC) to enhance real-time performance and data accessibility. The system was implemented and tested in a controlled laboratory setting, where its performance was compared to both local and centralized processing systems. Key performance metrics, such as latency and reliability, were measured over multiple iterations. Findings The hybrid system demonstrated latency comparable to local processing but significantly lower than centralized systems. It also maintained reliable performance with minimal variability and remained resilient to network disconnections. These characteristics make the proposed hybrid system highly effective for real-time monitoring in dynamic construction environments. Originality This study presents a novel hybrid architecture that uniquely combines the strengths of both local and centralized PWS, offering an optimal balance between real-time responsiveness and robust data integration. The integration of edge computing with combination with IFC for construction safety monitoring is an innovative approach that has not been previously studied. Research limitations Further research is required to validate the system in real-world construction sites and to address potential time delays in cross-system data access.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".