Developing a Digital Twin-based Framework for Construction Fire Hazard Recognition Training
Bibliographic record
Abstract
Construction sites are dynamic environments where fire hazards pose significant safety risks.Effective hazard recognition is the key step to prevent such hazards.While existing research has developed various training methods, limited studies focus on fire hazards in construction.Moreover, most approaches fail to adapt to evolving site conditions.To address these gaps, this study proposes a digital twin (DT)based framework for dynamic fire hazard recognition training, with current validation progress presented.This framework integrates 360° imagery, 3D Gaussian Splatting, and Immersive Virtual Reality (IVR) to create an adaptive and interactive training environment.A user-centered training approach is designed to enhance personalized learning by incorporating individual trainee profiles and situation awareness (SA) assessments.Additionally, a cloud-based data system enables long-term tracking and scenario updates based on historical hazard data and trainee performance.This modular conceptual framework provides a foundation and guideline for future research on dynamic fire hazard recognition training.Further work will focus on framework validation through pilot studies in real construction settings to assess training effectiveness and usability.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".