Genetic Algorithm–Driven Affordance Detection Technology Based on Posture Primitives for Atypical Architectural Surfaces

Key Points

• The study aims to optimize human behavior simulations in atypical architectural spaces using genetic algorithms based on posture primitives.
• Development of an affordance detection technique from atypical architectural forms.
• Utilization of genetic algorithms to explore postural arrangements on complex surfaces.
• Integration of the developed technique into Rhino and Grasshopper software for visualization and analysis.
• The proposed method allows for the dynamic prediction of human behaviors based on spatial conditions.
• A quantitative assessment of architectural morphological characteristics' impact on human behavior was achieved.
• The technology showed potential as a design support tool for optimizing spatial arrangements.

Abstract