Project risk assessment: A holistic risk identification, analysis and evaluation approach, The case of EPC projects
Bibliographic record
Abstract
This study presents a comprehensive framework for Project Risk Management (PRM), tailored specifically for Engineering, Procurement, and Construction (EPC) projects. Addressing gaps in traditional risk assessment methodologies, the proposed approach integrates advanced techniques for risk identification, analysis, and evaluation based on risk characteristics. A three-stage framework is proposed utilizing the Delphi method for risk identification and contextualization of risks, the risk analysis stage employs the Fuzzy Level-Based Weight Assessment (F-LBWA) method to achieve fuzzy weights for risk characteristics which the risks will be evaluated by. The final evaluation stage uses the Fuzzy Combined Compromise Solution (F-CoCoSo) method to rank risks, categorizing them as threats, opportunities, or hybrids. A case study of an EPC project demonstrates the framework’s practical application, highlighting construction-phase risks as the most critical threats (negative risks) while also emphasizing opportunities (positive risks) which can be exploited. By incorporating fuzzy logic and innovative Multi-Criteria Decision-Making (MCDM) methods, the framework provides a flexible and robust tool for modern PRM.
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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 | 0.023 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".