Application of fuzzy logic to quality assessment of infrastructure projects at conceptual cost estimating stage
Bibliographic record
Abstract
This paper describes a model to assess the quality of infrastructure projects at the conceptual cost estimating stage based on the extent to which a project exhibits the ideal relationships between the quantities and costs of each of its components, compared to an ideal project that meets the requirements of an organization. The output of the model is a quality score that can be used to compare a project against others in an organization. The model is intended for use by organizations in the project planning phase to help identify potential scope and (or) design deficiencies. A fuzzy expert system is used to model the relationships between the physical characteristics of a project and the expected quality using a cost ratio comparison between an ideal project (i.e., a cost model) and the project being compared. The fuzzy expert system provides the advantage of allowing assessments to be made in linguistic terms, which suits the way in which experts express themselves and captures heuristic knowledge of the experts in assessing the quality of a project at the conceptual cost estimating stage.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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".