THE INTEGRATION OF A SCAN-TO-HBIM PROCESS IN BIM APPLICATION: THE DEVELOPMENT OF AN ADD-IN TO GUIDE USERS IN AUTODESK REVIT
Bibliographic record
Abstract
Abstract. In recent years, the generative process of building information modeling (BIM) digital models oriented to the digitisation of heritage buildings has been supported by the development of new modeling tools, able to integrate the point cloud data produced by laser scanning and digital photogrammetry in major modeling software applications such as Autodesk Revit and Graphisoft Archicad. Architectural and structural elements of churches, castles, and historical monuments such as complex vaults, arches, decorations and ornaments, irregular walls with a variable section and wall stratigraphy require higher levels of detail (LOD) and information (LOI) than new buildings. Consequently, the structure of a BIM model oriented to represent heritage buildings (HBIM) required the definition of a new digital process capable of converting the traditional techniques to the generation of 'unique' digital models able to connect different type of information. Consequently, the generation of 'new' 3D objects able to follow the constructive logic of the detected artefact has required the establishment of new grades of generation (GOG) and accuracy (GOA) to reduce the time and cost of the scan-to-BIM process. The main challenge of this research was the integration of these new modeling requirements in BIM software through the development of an add-in for one of the most used BIM software (Autodesk Revit). Through the generation of the complex vaulted system of the Basilica of Collemaggio (L’Aquila, Italy) and one of the most famous monuments of northern Italy (Arch of Peace in Milan, Italy), the following research shows how it was possible to support users in the HBIM generation, reducing the modeling impact of complex shapes from point cloud data and increasing information sharing for different BIM-based analysis, disciplines and users.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".