Procedural Modeling-Based BIM Approach for Railway Design
Bibliographic record
Abstract
Building information modeling (BIM) is a powerful methodological approach for designers that has revolutionized the field of architecture and construction for some years now, minimizing errors and making the entire design, construction, and management process more efficient. The first results have been so encouraging that many countries, from Europe to the United States to Asian countries, have adopted specific regulations to promote its development and use. BIM models are based on the Industry Foundation Classes (IFC) standard, i.e., an object-based file format with a data model developed by building SMART to facilitate interoperability. Objects are characterized by properties, such as geometry, material, cost, and all related construction process information, such as scheduling or the maintenance process. The 3D modeling of these objects geometric information is parametric, in order to make the design more flexible. This research work offers an insight into the possibilities offered by different BIM-based tools for parametric modeling applied in the railway sector whereby an example of a railway section model is presented. Indeed, the focus will be on the creation of parametric objects representing railway components, as existing BIM object libraries lack them in the IFC2 × 3 standard format.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".