Fuzzy Contingency Determinator<sup>&#x00A9;</sup> a fuzzy arithmetic-based risk analysis tool for construction projects
Bibliographic record
Abstract
Construction companies always try to employ practical tools to assess risks and determine project contingency, yet the uncertain nature of risk makes it difficult to ensure that the assessment process is accurate or the agreed-upon project contingency is sufficient to deal with such uncertainty. This paper introduces a novel contingency determination procedure that employs fuzzy arithmetic in carrying out its computational steps. Fuzzy linguistic scales are used in this procedure to help experts assess the probability and impact of events using linguistic terms that are described by fuzzy numbers instead of inaccurate, single crisp values. The introduction of fuzzy logic helps experts use their own judgment-rather than relying on historical data-in determining contingency that deals with the shortcomings of applying the probabilistic risk analysis approaches in the absence of historical risk data. The paper also illustrates a new user-friendly software tool, namely, Fuzzy Contingency Determinator, which has been developed to carry out the steps of the procedure. The main contributions of this paper are (1) the introduction of a new contingency determination procedure, based on fuzzy logic, which can handle the subjective uncertainty of experts in assessing critical events leading to risk and opportunity and in determining contingency, and (2) the implementation of the procedure using a simple and user-friendly software tool.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".