Quantitative and qualitative risk in EPCM projects using fuzzy set theory
Bibliographic record
Abstract
The importance of risk management for engineering, procurement and construction management (EPCM) projects has been progressively recognized over the last two decades. Researchers and practitioners alike introduced a wide range of methods to analyze and evaluate risks. Several methods were developed using fuzzy set theory in view of its capabilities for modeling projects risk quantitatively and qualitatively, which assists in providing more representative modeling and evaluation of project risk. This research focuses on fuzzy set theory and presents a methodology to analyze the risks associated with EPCM projects quantitatively and qualitatively. The proposed method implements a linguistic representation for risk which gives experts the choice to evaluate the risk linguistically or numerically based on their experience. The proposed method also presents a systematic procedure to convert the linguistic evaluations into numerical evaluations. The converted numerical evaluations should be aggregated using a screening procedure, based on several rules implemented by users, in order to combine the numerical and linguistic evaluations. This combination uses the arithmetic operations of fuzzy set theory in order to calculate the fuzzy mean of identified components of risk (e.g. each risk has one fuzzy representation). The generated fuzzy evaluations of risk are also deffuzzified using one of the defuzzification methods (e.g. Center Of Area) commonly used in literature. A case example is presented to investigate the effectiveness of proposed method followed by a discussion of results and conclusions.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".