Fuzzy System Dynamics for Modeling Construction Risk Management
Bibliographic record
Abstract
The unique nature of construction projects and uncertainties during project execution make construction a highly risk-prone industry. The system dynamics (SD) approach, which focuses on the cause-effect relationship of model variables, is a viable option to model and analyze construction risks, which are considered to be highly dynamic, and has the ability to capture the interrelationships and interactions among different risks. However, conventional SD has a limited ability to handle risk imprecision and uncertainty; these elements can be best dealt with using fuzzy logic. Research endeavors to integrate SD and fuzzy logic so as to address the shortcomings of SD in construction risk modeling and analysis are very few. In this paper, a methodology for developing a fuzzy system dynamics (FSD) framework is proposed that combines the strengths of SD with those of fuzzy logic to improve construction risk modeling and develop risk mitigation strategies. The main contributions of this paper are: (1) identifying research gaps in FSD modeling; (2) providing a systematic and detailed methodology for developing the FSD framework; and (3) examining existing approaches for representing fuzzy variables and fuzzy rules, and the impact of different fuzzy arithmetic operators and defuzzification methods in FSD models.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".