Yang, Y.; Luo, H. and Adibhesami, M. A. (2026) Climate and performance-driven architectural floorplan optimization using deep graph networks. Engineering, Construction and Architectural Management, 33(3), pp. 2400-2421. ISSN 0969-9988
Abstract
Purpose – This study introduces a novel approach to generating and optimizing energy-efficient and climate-responsive architectural floorplans. Design/methodology/approach – The DGraph-cGAN model utilizes advanced deep-learning techniques to produce diverse, realistic layouts that meet specific design constraints and functional requirements. Findings – The results show significant energy savings (32.1% overall) across different building types and climate conditions, with reductions in energy use intensity, CO2 emissions and annual energy costs. Case studies demonstrate notable improvements in energy savings, CO2 emission reduction, daylight autonomy, thermal comfort and cost savings. Practical implications – The DGraph-cGAN model has great potential for advancing architectural design optimization, with opportunities for further refinement and application in various contexts. Originality/value – This study contributes to developing a novel approach to optimizing architectural floorplans using deep learning techniques. It provides a valuable tool for architects and designers to create energy-efficient, climate-responsive buildings.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | architectural floorplan generation; climate-responsive; deep graph conditional generative adversarial network; energy efficiency; performance optimization |
| Index terms: | energy efficiency, CO2 emissions, case study, floorplan, deep learning, designer, energy-saving, architect, thermal comfort, cost saving, energy-use intensity, energy cost, methodology, architectural design |
| Subjects: | artificial intelligence, energy systems, sustainability and energy, air quality, design layouts, data collection methods, profession, cost management, environmental engineering, economics, design practice, research methods |
| Topics: | Design Practice, Digital Applications, Roles and Professions, Research Practice, Cost Management, Sustainability |
| Descriptive scope: | 4 PCTE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here