Value Engineering and Decision-Making Process in Façade Project Development: A Real Estate Case Study
Bibliographic record
Abstract
This study explores the application of Value Engineering (VE) and Life Cycle Costing (LCC) in the development of a LEED Gold-certified office building in São Paulo, Brazil. The project, characterized by high-end real estate design and sustainability objectives, involved a thorough examination of façade material options, particularly precast concrete panels. The methodology integrates SWARA and WASPAS frameworks for evaluating façade materials, leveraging Building Information Modeling (BIM) technologies for 4D and 5D modeling to enhance decision-making. Key findings highlight significant cost savings through VE, achieving a 27% reduction in initial costs by optimizing façade panel design and crane operations. LCC analysis revealed a comprehensive understanding of the financial implications over a 60-year lifespan, contrasting precast concrete with thermal insulating coatings. This study underscores the importance of concurrent design processes in real estate projects, emphasizing the need for early contractor involvement and transparent cost management strategies. The findings contribute to improved decision-making frameworks in sustainable real estate development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".