Value Engineering and Decision-Making Process in Façade Project Development: A Real Estate Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it