Implementation of a BIM Solution in a Small Construction Company
Bibliographic record
Abstract
The challenges to produce with more quality, less cost and less leading time, drove the construction sector to find new process and tools to help them achieve these goals. Building Information Modeling (BIM) is a solution for these demands supported by many researchers and companies. Many enterprises had already started the BIM adoption process and many studies have been conducted. Unfortunately, the majority of studies are focused in big companies and developed countries, leaving medium and small companies, especially the ones in developing countries, without data to analyze the feasibility and advantages to enter this process and to guide them through it. In light of this, this paper provides a description of a BIM deployment process in a small construction company in Brazil. The case study presents an implementation process which includes seven steps, some of them still ongoing. Even with an unfinished BIM deployment process that was carried on without any known methodology, benefits derived from using BIM were observed and barriers for its full implementation were identified. Comparing these findings with the literature review, it may be noted that even if the size and country differs, most of the benefits and barriers are similar.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".