Integrated ISM-Fuzzy MICMAC approach based factor analysis on the implementation of safety program in construction industry
Bibliographic record
Abstract
The challenge of improving construction safety performance is observed in many countries. Safety is considered by practitioners and researchers as an important topic in construction industry sites. Despite the findings of implementing safety programs, it is revealed that accidents and injuries are not perfectly reduced in construction projects. In the literature, authors tried to establish several frameworks and proposed methods to reach this objective by identifying the key factors affecting safety performance. The aim of this study is to present critical factors used in the implementation of safety programs and to explore their relationships using Interpretive Structural Modeling (ISM). Then, via ISM technique, the overall structure among factors was revealed. By using the Fuzzy MICMAC analysis, the factors were classified into four groups based on their driving power and dependence power. The results showed that “Safety Training” and “Management Commitment” have the most important impact on safety programs, but also it is very important to study the interactions among factors at different stages. This analysis offers key resources for practitioners and decision makers by analyzing the relationships between factors and its driving or dependence strength. These results shed lights on the effective development of measures to facilitate the implementation of safety programs in the construction sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".