Affordance Detection in Atypical Architectural Spaces Using Behavior Pattern Recognition

Abstract

Atypical architectural spaces present significant challenges for predicting and evaluating human behaviors during the architectural design process. Conventional human behavior simulation methods often rely on predefined rules or agent-based pathfinding approaches, which have limitations in representing the complex relationships between spatial form and potential human actions in highly irregular environments. To address this issue, this study proposes an affordance detection approach for atypical architectural spaces using behavior pattern recognition. The proposed method focuses on extracting geometric patterns associated with human behaviors and utilizing them to identify spatial affordances in newly designed spaces. First, spatial locations where human behaviors are successfully placed in atypical environments are analyzed to extract surrounding geometric configurations. These configurations are formalized as behavior-inducing geometry patterns and organized into a structured behavior pattern database. Each geometry pattern is linked to a corresponding human behavior model, establishing a relationship between spatial form and potential human actions. Based on this database, behavior pattern recognition is applied to analyze newly designed atypical architectural spaces by searching for geometric configurations similar to the stored patterns. When a matching pattern is detected, the associated human behavior model is automatically assigned to the corresponding spatial location. Through this process, the system identifies spatial regions where specific human behaviors are likely to occur, effectively detecting spatial affordances in atypical architectural environments. Implemented within the Rhino and Grasshopper computational design environment, the proposed approach supports behavior-informed architectural design by enabling designers to evaluate potential human behaviors during the design process.

Keywords: Affordance Detection, Human Behavior Simulation, Atypical Architecture, Behavior Pattern Recognition

DOI: 10.54941/ahfe1008090