CEPM 1: special purpose simulation modeling of tower cranes
Bibliographic record
Abstract
Historically, simulation tools have only been used and understood by the academic community. Special Purpose Simulation (SPS) techniques have introduced computer modeling to the industry, resulting in reduced model development time and a user-friendly environment. This paper describes the special purpose simulation template, which is based on the tower crane operations performed by PCL Constructors Inc. On-site management of the tower crane resource is based on prioritized work tasks that need to be performed within a set period of time. Traditional SPS modeling techniques use 'relationship logic links' to represent the logic contained in the modeled system. As the number of work tasks increases for the tower crane resource, the model complexity using traditional simulation techniques becomes unmanageable, resulting in limited acceptance by industry practitioners. The tower crane template uses 'priority rating logic' to replace the 'relationship logic links'. Evaluation of the tower crane operations at the Electrical and Computer Engineering Research Facility (ECERF), being constructed in Edmonton, is used to illustrate the advantages of using the 'priority rating logic' modeling approach for tower crane operations. The simulation model analyzes the ECERF tower crane production cycle yielding outputs for total duration, crane utilization, and lift activity hook-time analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".