Defining Building Information Modeling implementation activities based on capability maturity evaluation: a theoretical model
Bibliographic record
Abstract
Building Information Modeling (BIM) has become a widely accepted tool to overcome the many hurdles that currently face the Architecture, Engineering and Construction industries. However, implementing such a system is always complex and the recent introduction of BIM does not allow organizations to build their experience on acknowledged standards and procedures. Moreover, data on implementation projects is still disseminated and fragmentary. The objective of this study is to develop an assistance model for BIM implementation. Solutions that are proposed will help develop BIM that is better integrated and better used, and take into account the different maturity levels of each organization. Indeed, based on Critical Success Factors, concrete activities that help in implementation are identified and can be undertaken according to the previous maturity evaluation of an organization. The result of this research consists of a structured model linking maturity, success factors and actions, which operates on the following principle: once an organization has assessed its BIM maturity, it can identify various weaknesses and find relevant answers in the success factors and the associated actions.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".