Building information modelling demystified: does it make business sense to adopt BIM?
Bibliographic record
Abstract
Purpose The purpose of this paper is to inform project management practice on the business benefits of building information modelling (BIM) adoption. Design/methodology/approach 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. The selected case study projects included a range of public (1) and private (4) sector building developments of small and large‐scale. Findings are 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. Findings Shared understanding on business drivers to adopt BIM for managing the design and construction process of building projects raging from small commercial to high‐rise. Originality/value The originality of the research reported in this paper is that it breaks from a proliferating series of articles on BIM as industry “aspiration” and as a “marketing” statement. The elicited drivers for BIM underwent industry, academic and peer validation.
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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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".