An Ontological Study on the State of the Turkish Construction Industry and its Implementation of Building Information Modeling (BIM)
Bibliographic record
Abstract
Due to the inconvenience of old and traditional ways of work in the Architecture, \nEngineering and Construction (AEC) industry, Building Information Modeling \n(BIM) was introduced and is considered as one of the most beneficial and successful \ndevelopments in the industry since it provides accurate virtual models for digital \nconstruction as well as it is best known for reducing mistakes and errors by \nimproving the coordination between various design areas.\nBIM technology is progressively being implemented in many countries, however \nthere is contrast in the usage and awareness among these countries in which some of \nthem appear to utilize and be aware of such technology more than others. In the case \nof a country like Turkey, it falls behind other countries like Finland and Canada \nwhen it comes to advanced implementation of BIM in construction. For this reason, \nan ontological study on the state of the Turkish construction industry and its BIM \nimplementation is carried out to investigate the work processes in the industry and to \nsee how frequently BIM is implemented. A quantitative research methodology is \nadopted in this study to acquire statistical information about the work processes in \nthe Turkish construction industry and its BIM implementation. At the end of this \nstudy, BIM implementation in Turkey is found to be limited as it is rarely used by the \nTurkish construction industry. Moreover, an ontological framework for BIM \nimplementation in Turkey is created which can be used to aid Turkish construction \ncompanies in adopting and implementing BIM in a more effective way.\nKeywords: BIM, Ontological framework, Turkey, Turkish construction industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".