GENERATIVE MODELING, VIRTUAL REALITY AND HBIM INTERACTION: IMMERSIVE ENVIRONMENT FOR BUILT HERITAGE: CASE STUDY OF SHAIKH ISA BIN ALI HOUSE, BAHRAIN
Bibliographic record
Abstract
Abstract. The innovation in Digital Cultural Heritage (DCH) shows an emergence of innovative methods and initiatives for the generation of complex historical models to which are linked useful information for specialists of architecture, construction and engineering, but also user community interested in cultural heritage. This paper presents an improvement in Historic Building Information Modeling (HBIM) turned into models for mobile apps based on virtual reality (VR) addressed to enhance the communication of Architectural Heritage. The opportunity of a collaboration with the Bahrain Authority for Culture and Antiquities (BACA) experts under the coordination of the Carleton University in a training project aimed at improving documentation skills for conservation and preventive maintenance of built heritage provided the availability of an extraordinary richness of data and surveying on the Shaikh Isa Bin Ali House. The case study of the Shaikh Isa Bin Ali House is an outstanding example of Bahrain architecture and one of the oldest surviving building in Muharraq, listed under the UNESCO sites, now transformed in a Museum, where the visit does not allow to perceive the richness of the traditions of the constructions, beside the beautiful architecture. The building was the seat of the ruler of Bahrain from 1869 until his death in 1932 and represented an interesting response to local building materials and climate. This vernacular architecture solved not only the climate problems but combined the solution with beauty, physical and social functionality. This research carries out digital preservation through HBIM and switches to Cloud and VR platforms to allow users to enter a 3D immersive scene, using models as a vehicle of content information.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".