Advanced Visualization of Discrete-Event Systems Specification in 3D Building Information Modeling using Autodesk Forge Platform
Bibliographic record
Abstract
Achieving high building performance is integral to modern architecture, encompassing not only aesthetic considerations but also critical aspects of energy efficiency and sustainability.Modern buildings must integrate advanced design principles and energyefficient Heating, Ventilation and Air-Conditioning systems to ensure optimal performance while maintaining occupant comfort.Addressing performance issues post-construction can be both challenging and expensive.To mitigate such risks, it is advantageous to evaluate and simulate building performance during the pre-design phase.This thesis introduces an integrated approach combining Discrete Event System Specification (DEVS) with Building Information Modeling (BIM) to enhance performance prediction and evaluation.By combining DEVS for detailed simulations with BIM for comprehensive modeling, this approach enables accurate pre-design assessments, reducing costly after-construction adjustments.Two case studies demonstrate the effectiveness of this approach in improving building efficiency, supporting high-performance buildings, and smart city goals.The integration also facilitates advanced design experiences, including gamification and virtual reality, offering new insights into building performance analysis and optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".