Fuzzy Logic and Fuzzy Hybrid Techniques for Construction Engineering and Management
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".