Assessing the performance of the building information modeling (BIM) implementation process within a small specialty contracting enterprise
Bibliographic record
Abstract
The current shift to building information modeling (BIM) enabled project delivery in the construction industry is promising important benefits. For small and micro businesses, which represent 99.0% of the employers in the Canadian construction industry, adopting these trends could significantly impact their bottom line. However, this often represents considerable cost and risk. Assessing the performance of BIM implementation therefore becomes an important part of the process, namely in ensuring that it is on track and progressing as required. This article presents the findings from a case study research project conducted over a 2 year period within a small mechanical contracting firm. The objective of this research project was to develop an evolutionary approach, supported by specific measures, to assess the performance of the BIM implementation process within a specialty contracting small enterprise. The findings suggest that BIM has had a positive impact over time on predictability for indicators such as total project cost and labor cost. On the other hand, project scope and quality were not shown to be influenced by BIM in the projects studied. The variability uncovered in the findings reinforces the central tenant of BIM as an enabler for collaboration. Indeed, most of the projects studied were performed in a lonely manner and thus the measured impact of BIM on project delivery was limited, even if it was perceived as very beneficial. Lastly, the article highlights the need for a parallel reconfiguration of practice: performance assessment and BIM implementation need to be developed conjointly to serve one another.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".