Enhanced façade design: A data-driven approach for decision analysis based on past experiences
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
The selection of an optimal building façade system is a challenging process that can be facilitated by using decision-analysis methods. However, current commonly-used decision-analysis tools in civil engineering cannot deal with the interactions among multiple design criteria. The Choquet integral is the only well-known method capable of accounting for such interactions. However, the process of assigning the fuzzy measures (importance weights) for this method is complex, particularly when there is a large number of criteria be considered. This paper proposes two supervised methods to estimate these fuzzy measures. The first method estimates the relative importance weights by using a statistical approach based on Principal Component Analysis, while the second method is elicited from a machine learning algorithm using Neural Networks. These two methods are used in an illustrative example to find the fuzzy measures related to façade design with respect to four criteria; and their merits and limitations are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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