Site-specific Modular Design Optimization for High-rise Residential Buildings
Bibliographic record
Abstract
Modular design refers to a design approach whereby customized modules or components are assembled to form the layout plan of a building. Previous researches have attempted to optimize the layout plan design of low-rise houses for maximizing the natural daylighting, ventilation performance, and energy efficiency. Engineers have also studied the modular design of high-rise residential buildings to meet site constraints and to optimize site development potentials. However, the previous studies on modular building design were based on empirical trial-and-error approaches, efficient methods for identifying the optimal combination of different modules and components were still lacking in literature. Therefore, this study attempts to develop an innovative approach for optimizing the modular design of high-rise residential buildings, with the aim of maximizing the building energy performance while fulfilling the site constraints and design code requirements. The design optimization problem, including the design variables and objective functions, is properly formulated to guarantee the quality of final optimized deign. Provided a set of well-defined modules and components, evolutionary genetic algorithm (GA) is then utilized for the wide-ranging exploration of the building layout plans, taking into consideration the site conditions and building design requirements. A computer program is developed, coupling the GA optimization and energy modeling, to systematically evaluate the candidate layout plans. The energy simulation results are subsequently used to guide the GA towards finding the optimal design solution. The proposed optimization method is utilized to generate the optimal layout design for a 40-story high-rise residential building, using a set of pre-defined modular flat units. The optimal design maximizes the use of natural ventilation and daylighting to save 30-40% of the energy consumption without compromising the site constraints and design requirements. The findings of this study serve as the decision support basis to enhance modular design of high-rise residential buildings (such as energy conservation in this study), thereby improving the sustainability and cost-effectiveness of the built environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".