Performance-Based Generative Architectural Design Integrating Environmental Constraints Using GANs
With the rapid advancement of deep learning technologies, generative artificial intelligence has become an influential tool in architectural design research and practice. While existing generative design studies largely emphasize spatial organization and functional relationships, environmental performance considerations are often treated as post-design evaluations rather than integral design drivers. This research addresses this limitation by proposing a performance-based generative framework that embeds environmental constraints directly into the architectural generation process.
Limitations of Conventional Generative Design Approaches
Most GAN-based architectural design models focus on visual similarity, spatial plausibility, or functional compliance, neglecting critical environmental performance metrics. As a result, generated design outcomes frequently require extensive post-processing or simulation-based refinement. This section discusses how the absence of performance feedback during model training restricts the practical applicability of generative design systems in real-world architectural decision-making.
Performance-Based GAN Framework
To overcome these limitations, the study proposes a modified Generative Adversarial Network (GAN) framework that incorporates environmental performance objectives into the training process. By embedding a performance-related evaluation term within the GAN loss function, the model aligns generative outcomes with both spatial-functional requirements and environmental quality targets. This approach marks a shift from purely data-driven generation toward performance-informed architectural synthesis.
Wind Environment Optimization as a Case Study
The proposed framework is validated through a case study focusing on wind environment optimization in a hospital lobby. A wind velocity uniformity index, calculated using a pre-trained wind performance prediction model, is integrated into the GAN training process. This section explains how indoor wind comfort becomes an explicit generative constraint, enabling the model to produce floor plans optimized for both usability and environmental performance.
Experimental Results and Performance Evaluation
Experimental results demonstrate that the proposed performance-based GAN significantly outperforms conventional generative models. Generated architectural solutions exhibit notably improved wind velocity uniformity and greater performance stability across samples. This section analyzes the quantitative and qualitative improvements, confirming the effectiveness of embedding environmental feedback into the generative learning process.
Implications for Early-Stage Architectural Design
The research highlights the potential of performance-driven generative models as intuitive and efficient decision-support tools for architects during early design stages. By simplifying the integration of environmental performance into generative workflows, the proposed method enables architects to explore high-performance design alternatives more effectively. This study contributes a practical pathway toward environmentally responsive generative design and advances the role of AI in sustainable architectural practice.
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