Good sustainability practices applied in buildings that use BIM technology
Bibliographic record
Abstract
The growing demand for more sustainable buildings, and the effectiveness in the execution of civil construction projects, give rise to the relevance of making due use of technology to increase construction work. Building Information Modeling (BIM) creates and uses the compressed computational data of a building project. This parametric knowledge is common in the work for risk management, document formulation, performance anticipation, cost estimation, problem solving and idealization. The problem question of the work was: “How can civil construction companies apply strategies through the BIM tool to reduce environmental impacts in the construction of buildings?”. The following general objective is to analyze the BIM tool applied to sustainability in the construction of buildings. This work was a bibliographic review. To define the work step by step, we used Bryman's recommendation (2008), which advises starting by understanding the topic, choosing information sources, data collection, data analysis, interpretation and proposal, and finally , result. Finally, it is worth highlighting that the use of BIM should not be considered a simple method of adopting a development model, but rather it is a multidisciplinary tool intended to correlate and enhance complex sectors in the design of sustainable engineering projects. .
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".