Developing Optimal Affordance Detection Technology Using Genetic Algorithm based on Posture Primitives on Atypical Surfaces

Abstract

This study presents an optimization method for human behavior simulation in atypical architectural spaces using genetic algorithms based on posture primitives. Traditional simulation tools often fail to capture diverse movement possibilities in non-standard environments, limiting their application in architectural design. To address this, we introduce a novel affordance detection technology that extracts potential human actions directly from architectural geometry rather than relying on predefined scenarios. Our approach employs genetic algorithms to refine optimal action placements on complex surfaces iteratively. A set of posture primitives is modeled based on anthropometric data, and their spatial suitability is evaluated through computational iterations. By integrating Rhino, Grasshopper, and ActoViz, our system enables designers to visualize and analyze possible human interactions within atypical architectural forms. The physics engine incorporated in the system introduces behavioral noise, allowing for a wider range of movement possibilities.The key contribution of this study is the automatic generation of spatial affordances for human behavior simulation. Unlike conventional methods, our approach does not require predefined action sets but instead derives possible movements dynamically based on spatial conditions. By systematically analyzing posture primitives and their adaptability to different surfaces, this research provides a data-driven foundation for predicting human behavior in complex architectural environments.Furthermore, the proposed method enhances the architectural design process by offering real-time feedback on spatial affordances, allowing architects to optimize layouts based on anticipated user interactions. The ability to generate realistic behavior simulations supports a more intuitive and human-centered design approach, making this research highly applicable to various architectural and urban planning contexts.By integrating computational affordance detection into architectural design, this study contributes to the advancement of human behavior simulation, enabling architects and designers to predict and refine spatial experiences with greater accuracy and efficiency.

Keywords: Affordance Detection, Genetic Algorithm, Human Behavior Simulation

DOI: 10.54941/ahfe1005971