BIM-integrated TOPSIS-Fuzzy framework to optimize selection of sustainable building components
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
Existence of diverse sustainable materials with distinctive features causes more difficult decision makings for project teams especially when it is intended to use ideal materials from diverse types of sustainable products. Few research studies have been conducted so far on applying Building Information Modeling (BIM) to act as Decision Support System (DSS) using of math works functions and tools in combination with Multiple Criteria Decision-Making (MCDM) techniques. The main purpose of this study is to propose a methodology that integrates BIM with decision-making and problem-solving approaches including Fuzzy and TOPSIS in order to efficiently optimize the selection of sustainable building components at the conceptual design stage of building projects. To select the optimum building components, each item is assessed against three major attributes of decision criteria as Design, Economic and Quality factors, which are applied in Multiple Attribute Decision Support System (MADSS) methodology to indicate the various performance of buildings sustainability. This BIM-integrated process is linked to the engine of Matlab software to apply Fuzzy functions on the users’ priority in order to automatically suggest the ideal solutions. The design alternatives suggested by Matlab is validated by Life Cycle Cost (LCC) method to analyze the operational cost of an actual building project. Using this innovative method will make the decision-making procedure more convenient as well as proposing more realistic and reliable final and sustainable optimized choice.
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 | 0.000 |
| 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