Building information modelling demystified: does it make business sense to adopt BIM?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it