Towards Sustainable Building Design: The Impact of Architectural Design Features on Cooling Energy Consumption and Cost in Saudi Arabia
Bibliographic record
Abstract
Energy-saving has become a high priority in the Architectural design of buildings, particularly in hot climate regions like Saudi Arabia. Thus, selecting the appropriate Architectural Design Features (ADFs) at the early design stage provides significant opportunity to manage heat flow, prevent excessive energy consumption, and maintain a comfortable temperature for the occupants. In this research, a structured “Architectural based-Energy Impact Scoring System (AEISS)” has been developed. The system incorporates seven key ADFs that were identified based on inputs from a large number of experienced architects, and embody 40 different design options. To support designers in selecting and evaluating, including energy analysis, of any Architectural design that has any combination of design options, AEISS incorporates a comprehensive decision scoring system. Energy analysis is performed using a simulation tool (Ecotect®) that is integrated with the Revit BIM models. To validate AEISS, three design alternatives were evaluated for a residential building in Saudi Arabia. Using AEISS, it was possible to arrive at the optimum design. This research presents a scientific decision-making approach to quantify design alternatives while reducing designers’ subjectivity in the evaluation process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".