Optimal Design and Modeling of Sustainable Buildings Based on Multivariate Fuzzy Logic
Bibliographic record
Abstract
The exemplary design of green buildings incorporates multiple correlated parameters that should be optimally selected and updated to ensure effective energy management and sustainable impact of building architecture on the ecosystem. In this paper, the adaptive modeling and development of such sustainable buildings with several unpredictable and qualitative attributes is implemented with multiple-input multiple-output (MIMO) fuzzy control system. The outputs of the proposed fuzzy corroborated sustainable building paradigm comprise of specific characteristics employed for assessing the optimal performance, viz. energy efficiency, user satisfaction, resources optimization, and environment quality. These diverse system criteria measured using the proposed fuzzy optimization model are plotted against their actual theoretical values for each data case. Furthermore, the sample dataset for the proposed sustainable building model is validated through simulation results in terms of mean absolute error and logarithmic quotient error in the estimation of various fuzzy output variables evolving with the dataset size.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".