Energy Feasibility with Bim of a Multifamily Building in Juliaca-Peru
Bibliographic record
Abstract
This study investigates the energy feasibility of a multifamily building in Juliaca, Peru, using the Building Information Modeling (BIM) methodology.Given that construction in the year 2020 contributed to 38% of CO2 emissions related to energy, it is crucial to address environmental impacts at all stages of the construction process.This study seeks to offer an accessible and sustainable solution by applying Spanish regulations and BIM methodology in its sixth dimension.Simulation is employed as an operational research technique, following the BIM workflow and parameters of the Method of Energy Certification Managed BIM (MECM-BIM).The results reveal that by defining the characteristics of the sixth BIM dimension for the multifamily building in Juliaca, Peru, a solid foundation for energy assessment is established, allowing for a comprehensive and detailed view of the multidisciplinary aspects of the building.Additionally, the design of the energy model represents a significant advancement towards the implementation of passive construction strategies, facilitating the identification of areas for improvement and optimization.Overall, these results confirm the energy feasibility of the multifamily building in Juliaca, Peru, using BIM methodology.It is concluded that the application of Spanish regulations and BIM methodology in construction projects in Juliaca, Peru, can lead to significant improvements in energy efficiency and sustainability of buildings, thereby contributing to the mitigation of environmental impact associated with the construction industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".