Implementation Framework for BIM Adoption and Project Management in Public Organizations
Bibliographic record
Abstract
Implementation Framework for BIM Adoption and Project Management in Public Organizations Giuseppe Miceli Junior, Paulo C. Pellanda and Marcelo de Miranda Reis Pages 114-121 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: The arrival of Building Information Modelling (BIM) platforms to the Architecture, Engineering and Construction (AEC) markets and companies has led to a significant increase in efficiency in this economy sector. However, many AEC companies try to implement BIM as a normal or incremental change or improvement in technology rather than as a technological paradigm shift that radically affects most organizational processes, which may hinder a successful adoption of BIM with a deployment of its full potential. This paper aims to present a basic BIM framework amenable to ensure a successful BIM adoption in public organizations, particularly adapted to the Brazilian federal government. Based on a literature review, we propose to split the factors into three main groups: project development procedures, model development procedures and public governance policies. Some processes were studied in order to infer which of them is necessary for a successful BIM adoption. A BIM implementation is expected to be a success only if all main groups of factors are well defined and the established goals for each of them are reached. We also describe results of two concept proofs related to actual cases of BIM processes implementation, with extensive changes in product and governance management processes. Main results show that any BIM adoption should consider the close relation among model management, product management and governance management groups. All these groups should be contextualized in the environment of any public organization. Keywords: BIM adoption; Collaborative Design; Building Information Modelling; Public Works. DOI: https://doi.org/10.22260/ISARC2019/0016 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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".