BIM maturity assessment and certification in construction project team selection
Bibliographic record
Abstract
Part of the 5th International/11th Construction Specialty Conference (ICSC 15). To implement BIM in a construction project successfully, all the project participants as BIM users must have minimum BIM capabilities. Before any project starts, assessing BIM capabilities of project stakeholders is a concern for construction clients. The main problem of public clients regarding BIM is that they have no mechanism to ensure that the key participants, they hire for a BIM project, have the minimum capabilities (i.e. BIM Infrastructure, processes and qualified resources) to participate in design and delivery of the project. The high variability of firms’ readiness to work with BIM may impose a high cost for the client and other mature members of the supply chain. Therefore, construction clients need a way to ensure minimum BIM maturity of participants, such as a maturity audit to assess BIM competency of potential project team members. From a client’s perspective, “minimum BIM qualification” means “minimum capability to use BIM”. The current BIM maturity models try to assess BIM capabilities level of firms, but lack BIM uses assessment. This research proposes a prototype that focuses on capability of firms in specific BIM uses, while measuring their general BIM capabilities. The research methodology is based on an iterative literature review followed by focus group discussions. Through literature review, the researchers propose BIM platform maturity model. Then, BIM experts discuss on possible improvements. It is expected that by using this model, construction clients may achieve more BIM benefits, i.e. reduced cost, time, and increased quality of project, through selection of BIM-qualified project team members.
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.
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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".