Mahyar Khayatkhoei Articles


Interactive Architectural Design with Diverse Solution Exploration

Glen Berseth, Brandon Haworth, Muhammad Usman, Davide Schaumann, Mahyar Khayatkhoei, Mubbasir Turab Kapadia, Petros Faloutsos

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.


Towards Computer Assisted Crowd Aware Architectural Design

Brandon Haworth, Muhammad Usman, Glen Berseth, Mahyar Khayatkhoei, Mubbasir Turab Kapadia, Petros Faloutsos

We present a preliminary exploration of an architectural optimization process towards a computational tool for designing environments (e.g., building floor plans). Using dynamic crowd simulators we derive the fitness of architectural layouts. The results of the simulation are used to provide feedback to a user in terms of crowd animation, aggregate statistics, and heat maps. Our approach automatically optimizes the placement of environment elements to maximize the flow of the crowd, while satisfying constraints that are imposed by the user (e.g., immovable walls or support bearing structures). We take steps towards user-in-the-loop optimization and design of an environment by applying an adaptive refinement approach to reduce the search space of the optimization. We perform a small scale user study to obtain early feedback on the performance and quality of our method in contrast with a manual approach.


Using synthetic crowds to inform building pillar placements

Brandon Haworth, Muhammad Usman, Glen Berseth, Mahyar Khayatkhoei, Mubbasir Turab Kapadia, Petros Faloutsos

We present a preliminary exploration of synthetic crowds towards computational tools for informing the design of environments (e.g., building floor plans). Feedback and automatic design processes are developed from exploring crowd behaviours and metrics derived from simulations of environments in density stressed scenarios, such as evacuations. Computational approaches for crowd analysis and environment design benefit from measures characterizing the relationships between environments and crowd flow behaviours. We investigate the optimization of environment elements to maximize crowd flow, under a range of LoS conditions, a standard indicator for characterizing the service afforded by environments to crowds of specific densities widely used in crowd management and urban design. The steering algorithm, the number of optimized environment elements, the scenario configuration and the LoS conditions affect the optimal configuration of environment elements. From the insights gained exploring optimizations under LoS conditions, we take steps towards user-in-the-loop optimization and design of an environment by applying an adaptive refinement approach to reduce the search space of the optimization. We derive the fitness of architectural layouts from background simulations. We perform a ground truth study to gauge the performance and quality of our method.