Application of a Fuzzy Decision Support System for Project Risk Management in Rail Transportation Infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article presents a fuzzy decision support system (FDSS) to enhance project risk assessment and decision-making in rail transportation infrastructure. Grounded in fuzzy logic theory, the FDSS evaluates risks by analyzing probability, impact, and mitigation strategies, thereby informing contingency levels for project budgets and schedules. By applying compositional rules of inference and incorporating real-world performance factors, the model reduces uncertainty and subjectivity common in traditional qualitative approaches. Drawing on the primary author's experience moderating over 3000 risk sessions in the rail industry, the study highlights how inconsistent and subjective assessments contribute to scope creep, cost escalation, delays, and cancellations. Although rail infrastructure teams typically possess strong technical expertise, they often lack specialized skills for rigorous risk management. The FDSS addresses this gap by producing reliable and replicable evaluations that increase confidence in decision-making. Enhanced fuzzy models, developed through Monte Carlo triangulation risk assessments, enable less-experienced staff to conduct simulations and achieve results comparable to seasoned experts. The system also automates the selection of critical project parameters, improving financial planning and schedule optimization. Its simplicity has been identified as a key factor in user adoption and effectiveness. A case study demonstrates the FDSS in practice, underscoring its adaptability and impact on reducing uncertainty and improving risk management outcomes in real-world transportation infrastructure projects.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it