Framework for Building Intelligent Simulation Models of Construction Operations
Bibliographic record
Abstract
Modeling and analyzing construction operations using simulation techniques allows researchers to capture the uncertainty and randomness usually associated with these operations and can thus be an effective tool for analysis and improvement. However, the effort and knowledge required to build simulation models and experiment with them tend to limit the use of simulation in construction. A common recommendation for removing this obstacle found in the literature leans towards developing simulation tools that reduce model development and experimentation time on the construction engineer’s side by packaging most of the knowledge required into the tool itself. Such “intelligent” simulation modeling tools may significantly impact the way construction engineers use simulation techniques in day-to-day decision making. This paper presents a framework that extends and formalizes this recommendation by providing the foundation for building intelligence into simulation objects. The proposed framework provides the structure necessary for building intelligence and autonomy into simulation objects and permits a further reduction in the knowledge required to experiment with simulation models. This approach also automates model modification, not only through changes in numeric parameters, but through topological model changes as well, which may assist the model user in making many decisions throughout the different phases of simulation experimentation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".