In architectural design, architects explore a vast amount of design options to maximize various performance criteria, while adhering to specific constraints. In an effort to assist architects in such a complex endeavour, we propose IDOME, an interactive system for computer-aided design optimization. Our approach balances automation and control by efficiently exploring, analyzing, and filtering space layouts to inform architects' decision-making better. At each design iteration, IDOME provides a set of alternative building layouts which satisfy user-defined constraints and optimality criteria concerning a user-defined space parametrization. When the user selects a design generated by IDOME, the system performs a similar optimization process with the same (or different) parameters and objectives. A user may iterate this exploration process as many times as needed. In this work, we focus on optimizing built environments using architectural metrics by improving the degree of visibility, accessibility, and information gaining for navigating a proposed space. This approach, however, can be extended to support other kinds of analysis as well. We demonstrate the capabilities of IDOME through a series of examples, performance analysis, user studies, and a usability test. The results indicate that IDOME successfully optimizes the proposed designs concerning the chosen metrics and offers a satisfactory experience for users with minimal training.

Crowd simulations provide a practical approach to evaluate building design alternatives with respect to human-centric criteria, such as evacuation times and flow in case of emergency scenarios. Coupled with Building Information Modeling (BIM) tools, they support architects’ iterative exploration of design alternatives. However, methods based on manually configuring a design and a corresponding simulation are not practical for exploring the potentially very large number of design solutions that satisfy human-centric design goals and requirements. Often, for practical reasons, designers may consider standard crowd configurations which do not capture the behavior of diverse occupants that may exhibit different locomotion abilities, movement patterns, and social behaviors. We posit that a joint exploration of high-dimensional building design and occupancy features is necessary to more accurately capture the mutual relations between buildings and the behavior of their occupants. To test this hypothesis, we conducted a series of experiments to automatically explore joint high dimensional design–occupancy patterns using an unsupervised pattern recognition technique (i.e. K-MEANS). We demonstrate that joint design–occupancy explorations provide more accurate results compared with sequential exploration processes that consider default design or crowd features, despite the longer computational times to simulate a large number of solutions. The findings of this case study have practical applications to the design of next-generation design exploration tools that support human-centric analyses in architectural design.

Abstract Authoring behavior narratives for heterogeneous multiagent virtual humans engaged in collaborative, localized, and task‐based behaviors can be challenging. Traditional behavior authoring frameworks are either space‐centric, where occupancy parameters are specified; behavior‐centric, where multiagent behaviors are defined; or agent‐centric, where desires and intentions drive agents' behavior. In this paper, we propose to integrate these approaches into a unique framework to author behavior narratives that progressively satisfy time‐varying building‐level occupancy specifications, room‐level behavior distributions, and agent‐level motivations using a prioritized resource allocation system. This approach can generate progressively more complex and plausible narratives that satisfy spatial, behavioral, and social constraints. Possible applications of this system involve computer gaming and decision‐making in engineering and architectural design.

We present a computational spatial analytics tool for designing environments that better support human-related factors. Our system performs both static and dynamic analyses: the first relates to the building geometry and organization, while the second additionally considers the crowd movement in the space. The results are presented to the designers in the form of numerical values, traces and heat maps displayed on top of the floor plan. We demonstrate our approach with a user study whereby novice architects have tested the proposed approach to iteratively improve a building accessibility in real-time with respect to a selected number of static and dynamic metrics. The results indicate that the users were able to successfully improve their design solutions and thus generate more human-aware environments. The usability and effectiveness of the tool where also measured, yielding positive scores. The modular and flexible nature of the tool enables further extension to incorporate additional static and dynamic spatial metrics.