Fuzzy Agent-Based Multicriteria Decision-Making Model for Analyzing Construction Crew Performance
Bibliographic record
Abstract
Selecting economically feasible policies for maximizing crew motivation and performance is a multifaceted problem, and each aspect of the process poses considerable unique challenges for construction practitioners. Fuzzy agent-based modeling (FABM) addresses some of the challenges of predicting crew performance (e.g., it accounts for both subjective uncertainties and crew dynamics), but strategy selection is a decision-making problem that is also compounded by expert disagreements, insufficient information, and differing stakeholder priorities. This paper proposes a methodology for integrating multicriteria decision-making (MCDM) with FABM to develop a decision support model that simulates the complex relationships and social interactions between crews and crew members for use in decision-making. This model also accounts for dynamic construction environments and captures the subjective factors that influence crew motivation and performance. The contributions of this paper are twofold. First, it proposes a methodology that will help improve decision-making processes in construction by expanding the scope of MCDM through integration with FABM. Second, it develops a fuzzy agent-based multicriteria decision-making model that helps construction practitioners adopt economically feasible strategies for improving the motivation and performance of construction crews. Furthermore, the proposed methodology can be adapted to several construction problems to help decision makers prioritize and select from several strategies intended to improve different crew performance measures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".