Fuzzy-Chaos Framework for Analyzing and Quantifying Uncertainty in Labour Productivity in Complex Construction Environments
Bibliographic record
Abstract
Effective management of labour productivity in construction projects is important for ensuring project success.This paper introduces a novel approach to quantifying uncertainty in labour productivity through entropy calculations based on outputs from a fuzzy expert system.Two distinct scenarios are analyzed to demonstrate the practical application of the proposed methodology, one representing a week with high variability and the other a stable and predictable week.These scenarios highlight how entropy can be a powerful tool for identifying periods of high uncertainty and assisting project managers in making informed decisions about resource allocation, risk management, and strategic planning.The findings suggest that high entropy values indicate weeks requiring increased management attention and resources, whereas lower entropy values correspond to more stable conditions, allowing for standard operational procedures.This approach enhances understanding of dynamic project conditions and supports proactive project management practices.This study contributes to the body of knowledge by integrating fuzzy logic with entropy calculations, which offers a robust framework for managing the complexities of labour productivity in construction projects.Through realworld application and comparative analysis, this study validates the effectiveness of entropy analysis as a diagnostic and decision-making tool in the construction industry.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".