Computerized DSS for evaluating design performance of residential buildings using additive weighting approach
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
Early architectural decisions have an enormous impact on the long-term performance of buildings. Evaluating architectural designs remains a subjective and biased process that varies from one person to another. Based on three questionnaires with 37 expert architects, seven Architectural Design Variables (ADVs) were identified, with a total of 40 design options that suit residential buildings. The experts defined the degree by which each ADV decision positively or negatively affect multiple life-cycle performance criteria: space functionality; construction time and cost; operational performance; and aesthetics. Based on that, this paper presents a novel Architectural Design Decision Support System (AD-DSS) to automatically evaluate and rank any combination of early design, using the Multi-Criteria Decision Analysis (MCDA) Simple Additive Weighting (SAW) method. The AD-DSS can automatically evaluate any combination of architectural design in terms of overall performance or a specific aspect without subjective inputs. Besides, a simple-user program developed to facilitate multiple system functions. A case study of a residential building with five different design alternatives demonstrated the powerful ability of the AD-DSS to provide early decision advice in a systematic and informed way to project stakeholders.
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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 | 0.001 | 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 | 0.000 | 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