Comparison of Two Simulation Platforms Based on Learning and Application Experiences of a Civil Engineering Trainee: SDESA versus SIMPHONY
Bibliographic record
Abstract
Simulation modeling remains relevant and crucial with the emergence of Artificial Intelligence (AI) and Industry 4.0. Analogous to designing production systems, planning construction methods needs to account for sufficient details and complexities of a construction operations system. General-purpose simulation platforms are intended to facilitate (1) modeling and analyzing construction operations and (2) designing, planning, and optimizing methods aimed at improving resource utilization, mitigating risks, and reducing efficiency losses. Nonetheless, creating and experimenting with construction process models on a simulation platform can be time-consuming and entail a steep learning curve, thereby hampering applications in practice. In this research, two construction simulation platforms were investigated, which represent the discrete-event-based simulation method and the simplified activity-based simulation method, respectively: SIMPHONY and Simplified Discrete Event Simulation (SDESA). In a comparative study, both platforms were utilized to solve typical construction operation problems by a graduate student trained in civil engineering who had no prior experience in computer programming and simulation. The time and effort required to learn the software and solve selected problems, along with the benefits of basic built-in tools and functionalities of the platforms, were critically compared.
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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 | 0.009 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".