Conceptual Model for Enhancing Construction Productivity Through Real-Time Physiological Monitoring and Fuzzy Hybrid Modeling
Bibliographic record
Abstract
This research presents a novel conceptual framework designed to tackle productivity challenges in Canada's construction sector, which are often affected by human factors such as stress, fatigue, and motivation.Utilizing fuzzy hybrid modeling and real-time physiological data captured via wearable sensors, this approach aims to transform the management of workers' physical and psychological states, thereby enhancing productivity.Traditional methods typically rely on subjective assessments and fail to accurately reflect the complex dynamics of construction sites and tasks.In contrast, the proposed method uses advanced sensor technologies to monitor real-time physiological responses, including heart rate variability, skin conductance, and brain activity.These metrics provide direct insights into the physical states of workers, enabling a more precise evaluation of their impact on productivity levels.The framework employs fuzzy hybrid modelling, which integrates fuzzy logic to address uncertainties and artificial intelligence techniques to analyze complex data patterns, creating a solid foundation for predictive analytics in dynamic construction environments.This innovative approach promotes a proactive management style, offering timely interventions that significantly enhance worker performance and overall project productivity.The study aims to develop a practical framework that guides construction managers in optimizing work processes and adapting strategies based on real-time physiological data, potentially revolutionizing industry practices by enabling more effective and informed decision-making.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".