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  Volume 3, Number 4, October 2018 (Special Issue) , pages 339-336

Precedent based design foundations for parametric design: The case of navigation and wayfinding
Vasiliki Kondyli, Mehul Bhatt and Timo Hartmann

    Parametric design systems serve as powerful assistive tools in the design process by providing a flexible approach for the generation of a vast number of design alternatives. However, contemporary parametric design systems focus primarily on low-level engineering and structural forms, without an explicit means to also take into account high-level, cognitively motivated people-centred design goals. We present a precedent-based parametric design method that integrates people-centred design \"precedents\" rooted in empirical evidence directly within state of the art parametric design systems. As a use-case, we illustrate the general method in the context of an empirical study focusing on the multi-modal analysis of wayfinding behaviour in two large-scale healthcare environments. With this use-case, we demonstrate the manner in which: (1). a range of empirically established design precedents - e.g., pertaining to visibility and navigation - may be articulated as design constraints to be embedded directly within state of the art parametric design tools (e.g., Grasshopper); and (2). embedded design precedents lead to the (parametric) generation of a number of morphologies that satisfy people-centred design criteria (in this case, pertaining to wayfinding). Our research presents an exemplar for the integration of cognitively motivated design goals with parametric design-space exploration methods. We posit that this opens-up a range of technological challenges for the engineering and development of next-generation computer aided architecture design systems.
Key Words
    human behaviour studies; navigation; wayfinding; architecture design; spatial cognition; visual perception; parametric design; architectural computing; design computing
Vasiliki Kondyli, Mehul Bhatt: DesignSpace Group.
CoDesign Lab – Cognition. AI. Interaction. Design.
Applied Autonomous Sensor Systems (AASS), Orebro University, Sweden
Timo Hartmann: Systems Engineering, Civil Engineering Institute, TU Berlin, Germany

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