Building information modelling demystified: Does it make business sense to adopt BIM
Bibliographic record
Abstract
Building Information Modeling (BIM) offers a revolutionising way to design, document and procure buildings. BIM promises to become a new international benchmark for building design and documentation across industry on the basis of improved efficiencies and collaboration capabilities. However, BIM requires rethinking current practices and process thus it calls for a paradigm shift in the way we procure, design and operate buildings. There seems no question that BIM methodologies are to become the norm in the long term but more factual evidence is required today to provide guidance to industry. This paper investigates current business drivers for BIM adoption by architecture and building engineering consultants. BIM needs to compete against well-ingrained methods to deliver projects in a fragmented and rather traditional industry. This paper investigates 47 value propositions for the adoption of BIM under a multiple case study investigation carried out in Australia and Hong Kong (Aranda-Mena et. al 2008). The selected case study projects included a range of public (1) and private (4) sector building developments of small and large scale. Findings were coded, interpreted and synthesised in order to identify the challenges and business drivers, and the paper focuses mainly on challenges and benefits for architectural and engineering consultants, contractors and steel fabricators. As a condition for the selection criteria all case studies had to be collaborating by sharing BIM data between two or more consultants / stakeholders. As practices cannot afford to ignore BIM this paper aims to identify those immediate business drivers as to provoke debate amongst the professional and academic community.
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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 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".