Solutions for scalability in building information modeling and simulation-based design
Bibliographic record
Abstract
Simulation-based design can enable a number of advanced architectural and engineering applications, such as energy modeling, occupant behavior prediction, or structural integrity analysis. To help make simulation-based design practical, scalability in terms of data and computation is needed. By using a Model-Driven Architecture (MDA) approach—together with the RISE (RESTful Interoperability Simulation Environment) web interface—a generic scalable simulation design framework is presented. In our system, Building Information Modeling (BIM) data is represented in the Industry Foundation Classes (IFC) open standard from which Domain Specific Models (DSM) may be extracted for particular applications. The open RISE interface to a DEVS (Discrete Event System Specification) simulation provides computational scalability. We present a case study in which our system is applied to an evacuation model of a multi-floor building. We also show a 3D visualization of the simulation results to support further decision making. By enabling designers to extract information automatically from IFC and run simulations remotely, this kind of scalable system makes simulation a viable part of the design process. 1.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".