Fuzzy Monte Carlo Agent-Based Simulation of Construction Crew Performance
Bibliographic record
Abstract
The use of agent-based modeling (ABM) in the analysis of construction processes and practices has increased significantly over the last decade. However, the developed models are not able to address both random and subjective uncertainties that exist in many construction processes and practices. Monte Carlo simulation is able to account for random uncertainty, and fuzzy logic is able to account for the subjective uncertainty that exists in model variables and relationships. In this paper, a methodology for the development of fuzzy Monte Carlo agent-based models in construction is provided, and its application is illustrated through the development of a model of construction crew performance. This paper makes three contributions: first, it expands ABM’s scope of applicability by showing how to model both random and subjective uncertainty in ABM; second, it provides a novel methodology for integrating fuzzy logic and Monte Carlo simulation in ABM, which allows for the development of fuzzy Monte Carlo agent-based models in construction; and third, it illustrates a fuzzy Monte Carlo agent-based simulation of construction crew performance, which improves the assessment of crew performance by considering both random and subjective uncertainties in model variables.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".