Fuzzy Logic and Fuzzy Hybrid Techniques for Construction Engineering and Management
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
Construction engineering and management are vital for successful project execution, and both researchers and practitioners continually seek ways to improve construction processes. Fuzzy logic plays an important role in many construction engineering and management applications, which are reviewed in this paper. This paper discusses the limitations of fuzzy logic and how this theory has been combined with other modeling techniques to develop fuzzy hybrid techniques, and describes the aspects of construction problems and decision making that are most effectively modeled using these techniques. Fuzzy hybrid techniques that are most common in construction are presented and examples from construction literature and the author's research program are provided. The author shares her vision of future research in this area, which is based on her expertise and experiences collaborating with construction industry partners, who have helped shape her research program and its impact on industry. Finally, the author presents her thoughts on the challenges construction researchers face in translating research to practice and measuring its impact, and she discusses some potential solutions from her research program. This paper is based on the 2019 Peurifoy Construction Advancement Address, which the author presented in Montreal, Canada, in June 2019.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it