Bibliographic record
Abstract
Over the past four decades, building modeling has taken numerous forms utilizing available technologies and software. Building information modeling (BIM) has significantly developed with the continuous advancements in the information technology and hardware industries. The evolution of BIM has attracted many researchers to explore its possible applications in modeling buildings and facilities with a humanitarian heritage value. However, researches have not been limited to that, as they later expanded to test the applicability of BIM in progressing the fields of maintenance and rehabilitation, operation and management, and even checking the durability of such buildings against varying circumstances and usability as well. As a result of a deep literature review, this research is developed to provide a critique of the previous studies conducted on the fields of heritage building information modeling (HBIM), the relevant software and equipment used in those studies, as well as case studies and applications used to demonstrate HBIM capabilities. Also, two case studies of Qasr Al Farid and Al-Bugiry buildings in KSA were implemented in this research to demonstrate the capabilities of HBIM in preserving the heritage value of historic buildings and monuments and to provide possible means of archiving the heritage value of those buildings utilizing state-of-the-art technologies. This study is expected to aid governments and decision makers of heritage buildings in understanding the positive impacts of including the HBIM in their management and operational processes, and will also act as a beneficial guiding tool for academic researchers to identify the gaps and limitations in previous studies to work towards overcoming them.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".