Architecture Engineers Award

In response to accelerating global challenges such as resource depletion, climate risk, and urban health inequality, architectural design is undergoing a fundamental shift from experience-based approaches to intelligence-driven practices. Artificial Intelligence for Sustainable Architectural Design (AI4SAD) has emerged as a critical catalyst in this transformation. This study provides the first comprehensive mapping of AI4SAD research, examining how AI is being applied across architectural design stages, sustainability objectives, and algorithmic domains. Scope and Methodology of the Systematic Review The research is grounded in a systematic review of 408 scholarly studies, offering a robust spatiotemporal overview of global AI4SAD development. By analyzing patterns across regions, time periods, design phases, and sustainability targets, the study establishes a structured evidence base that reveals dominant research trajectories as well as underexplored areas within AI-driven sustai...

  Architectural heritage is characterized by complex structural systems, long-term material aging, and evolving usage patterns, which result in diverse and often unpredictable damage mechanisms. These challenges are further compounded by external environmental factors that are difficult to control, increasing uncertainty in conservation efforts. This study positions artificial intelligence (AI) as a transformative tool capable of addressing these complexities by enabling data-driven, adaptive, and efficient heritage protection strategies. Characteristics and Challenges of Architectural Heritage Protection The paper highlights how architectural heritage differs fundamentally from contemporary buildings in terms of materials, construction techniques, and vulnerability to environmental stressors. Damage patterns in heritage structures often develop slowly and nonlinearly, making early detection and intervention difficult. These characteristics necessitate advanced analytical tools ...

  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...

With the growing integration of artificial intelligence in architectural practice, machine learning (ML) has emerged as a promising tool for enhancing design efficiency and decision-making. While ML techniques are well known for identifying complex patterns within large datasets, their application in predicting architectural design parameters—particularly for form generation—remains relatively underexplored. This study investigates the feasibility of a machine learning–based framework capable of predicting numerical design parameters to support the generation of architectural form in a controlled, human-centered design context. Complexity of Architectural Form Generation Architectural form generation is influenced by multiple interdependent factors, including spatial logic, functional requirements, and human-centered considerations. These factors introduce a high level of complexity that challenges conventional rule-based or deterministic design approaches. This section discusses ho...