Breaking-down building design problems with decomposition approaches: A review
Bibliographic record
Abstract
Decomposition simplifies complex building design problems by breaking them into smaller, manageable subproblems, enabling structured and efficient optimization. While widely used in systems engineering, its application in building design remains underexplored due to inconsistent definitions and a lack of structured guidelines. This review systematically examines decomposition approaches in early-stage building design optimization, primarily focusing on energy and emission performances. The study first characterizes single-level building design optimization problems and underscores the importance of decomposition. It then analyzes decomposition mechanisms, focusing on four hierarchical approaches: Sequential, Iterative, Nested, and Partitioned, along with a structured guideline outlining their key implementation criteria and challenges. Findings demonstrate that decomposition reduces computational effort while maintaining solution accuracy and enhances automation. This review highlights how decomposition improves design flexibility and supports the integration of operational performance in the early building design stages. The practical guideline enables key stakeholders to improve collaboration and facilitate a more informed decision-making 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".