Architecture Engineers Award

  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 f...

  The rapid evolution of AI in architecture is reshaping how early-stage design is conceptualized, analyzed, and communicated. Yet, despite its potential, the architectural industry still faces major obstacles in integrating AI into practical workflows. ArchiWeb emerges as a transformative web-based platform designed to bridge this gap by unifying data, processes, and computational intelligence. Its cloud-native, interactive environment supports lightweight data exchange and modular algorithms, enabling architects to embed AI-driven reasoning directly into conceptual design stages. By streamlining access to AI tools and fostering cross-disciplinary collaboration, ArchiWeb sets the foundation for a more intelligent, efficient, and environmentally responsible architectural future. The Need for Unified AI Integration in Architectural Practice Architectural workflows remain fragmented, especially when incorporating AI-based methods across varying stages of design. Traditional softw...