Relationship between Management Challenges and Construction Project Sizes Using Fuzzy AHP
Bibliographic record
Abstract
Residential building projects can be as simple as a single-story house or as complex as skyscrapers.How hard it is to manage these projects depends on how big they are.What aspects of projects get more challenging as they get bigger is still unknown.Thus, this paper delves into this subject and tries to identify these challenging factors in the emirate of Sharjah in the United Arab Emirates.To accomplish this goal, factors found in the literature were ranked by experts using the fuzzyanalytical hierarchal process (F-AHP).The identified factors were grouped into three categories.According to the F-AHP, technical challenges were the most challenging part of managing a large project.Interpersonal and operational factors came in second and third, respectively.First on the list of sub-factors was quality management, then the use of technologies.Surprisingly, the cost was ranked last.These results will help make it easier for decision-makers to match projects with project managers.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.002 | 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 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".