A hybrid approach for developing special purpose simulation tools
Bibliographic record
Abstract
The use of simulation techniques is an effective approach for modeling construction operations. Unfortunately, the high level of technical knowledge and development time required for building functional simulation models renders simulation modeling an impractical technology for many in the construction industry. Research in construction simulation tackles this conflict by providing modeling approaches that reduce the knowledge and time usually required for building simulation models of construction operations. Special purpose simulation (SPS) allows construction engineers with only minimal simulation knowledge to build practical simulation models. This paper presents a hybrid approach (HSPS) for effective and time-saving development of SPS tools. The approach utilizes visual, general purpose modeling elements to customize the simulation behaviors of new SPS elements, minimizing the programming effort required for developing these elements. This paper describes the theoretical background to the HSPS approach, its implementation, and a sample application successfully created subsequently. It also shows the results of an experiment quantifying the savings in development time achieved using this approach.Key words: automation, simulation models, computerized simulation, tunnel construction, construction management.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".