Hybrid fuzzy Monte Carlo agent-based modeling of workforce motivation and performance in construction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper aims to cover the development of a methodology for hybrid fuzzy Monte Carlo agent-based simulation (FMCABS) and its implementation on a parametric study of construction crew performance. Design/methodology/approach The developed methodology uses fuzzy logic, Monte Carlo simulation and agent-based modeling to simulate the behavior of construction crews and predict their performance. Both random and subjective uncertainties are considered in model variables. Findings The developed methodology was implemented on a real case involving the parametric study of construction crew performance to assess its applicability and suitability for this context. Research limitations/implications This parametric study demonstrates a practical application for the hybrid FMCABS methodology. Though findings from this study are limited to the context of construction crew motivation and performance, the applicability of the developed methodology extends beyond the construction domain. Practical implications This paper will help construction practitioners to predict and improve crew performance by taking into account both random and subjective uncertainties. Social implications This paper will advance construction modeling by allowing for the assessment of social interactions among crews and their effects on crew performance. Originality/value The developed hybrid FMCABS methodology represents an original contribution, as it allows agent-based models to simultaneously process all types of variables (i.e. deterministic, random and subjective) in the same simulation experiment while accounting for interactions among different agents. In addition, the developed methodology is implemented in a novel and extensive parametric study of construction crew performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it