BIM-integrated TOPSIS-Fuzzy framework to optimize selection of sustainable building components
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".