Sensitivity Analysis of Construction Simulation Considering Site Layout Patterns
Bibliographic record
Abstract
This paper presents a new construction simulation approach using Cell-DEVS techniques, which allows considering site spatial constraints and the sensitivity analysis of different site layout patterns. The proposed approach includes three phases: pre-processing to configure the simulation, main-processing where the simulation is executed, and post-processing, which incorporates animation and sensitivity analysis of the results. Using Cell-DEVS modeling, spatial resource allocation, worksite layout, as well as the movement of equipment can be explicitly analyzed and visualized. Spatial conflicts can be detected, resolved, and compared based on different site layout patterns. As a result, the accuracy of simulation is improved, especially for situations where spatial conflicts are critical. A case study is presented to illustrate the procedure for modeling and analyzing the sensitivity of simulation results considering several site layout patterns. The difference between the results of MicroCYCLONE and Cell-DEVS illustrates that in some cases the impact of spatial constraints on productivity is significant and should not be neglected. Based on the present study, it has been found that Cell-DEVS simulation is an effective tool for space-related analysis in construction project.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".