Modeling Framework and Architecture of Hybrid System Dynamics and Discrete Event Simulation for Construction
Bibliographic record
Abstract
Abstract: Construction system modeling aims to improve construction work performance by tracking the dynamic behaviors of construction systems. More accurate system modeling can be achieved by considering the mutual effects of construction operations on the context level of the system. Hybrid models of discrete event simulation (DES) and system dynamics aim to capture these mutual effects to provide model developers with more precise system analysis. Although system dynamics models are used to capture the behavior of the system at the context level, DES models are utilized to capture construction operations. The potential benefits of utilizing hybrid models for complex systems have been argued and established, but there are still limited studies that have utilized this modeling approach for real construction systems. In this research, we have attempted to identify the challenging issues that have caused this problem and to confront this problem by proposing a hybrid framework and architecture, which address these challenges. To verify the effectiveness of the new model, the performance of the proposed hybrid modeling framework and architecture has been tested by applying the proposed model in a real-scale construction-related system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".