Comprehensive Hybrid Framework for Risk Analysis in the Construction Industry Using Combined Failure Mode and Effect Analysis, Fault Trees, Event Trees, and Fuzzy Logic
Bibliographic record
Abstract
The nature of the construction industry is characterized by many risks and uncertainty inherent in every phase of the project life cycle. Risk management, therefore, is essential for a construction project to succeed in fulfilling its project objectives. In conventional event-tree analysis, the probability of the risk event, the probability of failure/success of different mitigation strategies, and the consequences of different paths must be assessed to allow for quantitative event-tree analysis. However, conducting quantitative event-tree analysis, especially in construction projects, entails several difficulties attributed to the lack of sufficient data. To overcome this challenge, this paper presents a comprehensive framework in which experts can use linguistic terms rather than numerical values to conduct event-tree analysis and calculate the expected monetary value (EMV) of risk events. The proposed framework is based on combining failure mode and effect analysis (FMEA), fault trees, event trees, and fuzzy logic. This paper allows experts to express themselves linguistically to calculate the EMV of risk events, which is more appropriate for the construction domain. In addition, this paper introduces a comprehensive framework for risk management that combines three well-known techniques in reliability engineering in a novel way that considers the often subjective quality of risk-related data. The application of fuzzy logic provides an effective tool to handle subjectivity in the construction domain. The proposed framework is implemented in the form of two software tools entitled Risk Criticality Analyzer and Fuzzy Reliability Analyzer. To validate the framework, a case study is presented and the EMV is calculated using the proposed approach. The result of the proposed approach is then compared to the result obtained using Monte Carlo simulation, demonstrating that the proposed framework gives similar results to Monte Carlo simulation but provides the advantage of allowing experts to express themselves linguistically, making the proposed framework more practical and easier to apply in the construction domain.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 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".