Bibliographic record
Abstract
Surveying BIM in the Lebanese Construction Industry Rita Awwad, Michael Ammoury Pages 963-971 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Building Information Modeling (BIM) has been gaining a significant edge in the construction industry over the last decade. BIM allows gathering all building information in one shared database that can help all construction entities better understand, integrate and visualize all work progress from inception to operation of the building. The main purpose behind BIM is to bring all project participants together (Client, Architect, Contractor, Consultant) since the initial stages of a project allowing them to cooperate and work as a team in the promise of an increased productivity, reduced cost, enhanced quality and faster delivery. However, a full-fledged implementation of BIM tools and benefits is not yet achieved in the construction industry and remains a debatable issue for researchers and practitioners in the construction field. This paper aims at assessing BIM awareness and usage in the Lebanese construction industry through conducting interviews with contractors, architects and consultants that are key players in the Lebanese market. A comprehensive literature review about BIM adoption in some developed countries and in the surrounding region is also provided in order to better evaluate the Lebanese industry status in comparison with foreign industries. This research sheds the light on BIM awareness of the different construction parties in the Lebanese market, assesses the extent of its current use, identifies the pertaining implementation challenges, and provides recommendations to enhance BIM role in the Lebanese construction industry. Keywords: Building documentation, BIM Awareness, Building information models, BIM challenges, Lebanese construction market DOI: https://doi.org/10.22260/ISARC2013/0105 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".