Overview of fuzzy simulation techniques in construction engineering and management
Bibliographic record
Abstract
Simulation has long been used in the construction domain to model construction systems. Simulation techniques (e.g., discrete event simulation, system dynamics, and agent-based modeling) are well-equipped to handle complex systems; however, they fail to account for the subjective uncertainties present in many construction systems. Fuzzy logic, on the other hand, is a powerful tool for dealing with subjective uncertainty; therefore, integrating these two techniques is advantageous in modeling construction systems. In this paper, we present an overview of simulation techniques in construction. Then, we introduce the advancements that have been made by incorporating fuzzy logic with simulation techniques, and we propose methodologies for developing fuzzy simulation models. Finally, we discuss the process of choosing a suitable simulation technique for construction modeling. In addition to providing an overview of simulation techniques accessible to the construction domain, the main contribution of this paper is in introducing methods for integrating fuzzy logic with simulation techniques.
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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".