Construction project proposal evaluation under uncertainty: A fuzzy-based approach
Bibliographic record
Abstract
Construction project proposal evaluations received much attention during the recent years due to increased awareness of sustainability, value for money, and transparency. The bid evaluation matrix is the commonly used method in the construction industry for project proposal evaluation. The subjective judgments used for evaluation criteria are associated with significant uncertainty. Moreover, the transformation of qualitative evaluations raises significant data uncertainty. Attempts have been made by previous researchers to incorporate uncertainties associated with project bid evaluations, but the ability of those methods to account for “true uncertainty” is questionable. The objective of this paper is to develop a fuzzy logic-based evaluation framework for project proposal evaluation. This use of type-2 fuzzy numbers sets the proposed approach apart from the current body of knowledge. An algorithm using type-2 fuzzy numbers was developed to define input uncertainty and parameter weights. The developed method was demonstrated using a building construction project case study. The proposed approach enables quantifying qualitative evaluations more comprehensively from a scientific basis, forming a generic proposal evaluation method applicable to various industry sectors.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".