Integrating Fuzzy Logic and agent-based modeling for assessing construction crew behavior
Bibliographic record
Abstract
Construction crew worker behavior emerges from interaction with fellow crew members, social influence, interaction with the environment, and personal characteristics (e.g., self-efficacy, goal commitment). Agent-based modeling is a good solution for handling complex systems of interacting agents and therefore is suitable for modeling construction crew behavior. Agent-based modeling can handle system complexities that arise from interactions of the system components; however, many systems-especially those comprising human behavior and social relationships-also include subjective uncertainties, which are not accounted for in agent-based modeling. Fuzzy logic, on the other hand, is able to deal with subjective uncertainty; therefore, for modeling behavioral and social systems such as construction crew behavior, integrating these two techniques is advantageous. In this paper, we present the concept of agent-based modeling, then we introduce the concept of integrating fuzzy logic and agent-based modeling. Finally, we present the development of a fuzzy agent-based model of construction crew behavior that will allow us to predict construction crew performance. The contribution of this paper is in introducing the integration of fuzzy logic and agent-based modeling in construction modeling and developing a fuzzy agent-based model of construction crew behavior.
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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 | 0.000 | 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 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".