An Exploratory Study of Building Information Modelling Maturity in the Construction Industry
Bibliographic record
Abstract
Despite the benefits of Building Information Modelling (BIM), the adoption level of BIM remains much lower than expected. Construction companies should appraise the existing condition in the BIM implementation to ascertain the applicable progress avenues that fit the user’s traits. To achieve this aim, the objectives of this paper are i) to identify the trends of BIM maturity studies ii) to conceptualise what is BIM maturity; iii) to identify the existing models of BIM maturity iv) to identify the indicators for measuring BIM maturity in the company, the project and the industry. A systematic review was conducted on BIM maturity articles, published in the Scopus database from 2008 to April 2018. The results reveal that most BIM maturity studies are dominated by authors from the United Kingdom and the United States, but the top three authors highly-cited were from Australia, Canada and the United Kingdom. The results highlight four aspects in the conceptualisation of BIM maturity: quality of use, the extent of use, the context of use and stages of the processes. The four most frequently quoted BIM maturity models are the National BIM Standard Capability Maturity Model, BIM maturity, BIM proficiency matrix and BIM implementation models. The results revealed seven major indicators for assessing BIM maturity namely information, people, policy, process, technology, organisation and BIM output. The findings advance the practitioners’ understanding of important indicators that must be considered to initiate or increase the BIM maturity levels in their respective companies or projects.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".