Simulation is an increasingly essential tool in the design of our environment, but any model is only as good as the initial assumptions on which it is built. This paper aims to outline some of the limits and potential dangers of reliance on simulation, and suggests how to make our models, and our buildings, more robust with respect to the uncertainty we face in design. It argues that the single analyses provided by most simulations display too precise and too narrow a result to be maximally useful in design, and instead a broader description is required, as might be provided by many differing simulations. Increased computing power now allows this in many areas.

Author: Sean Hanna
Author: Lars Hesselgren
Author:Victor Gonzalez
Author:Ignacio Vargas

Publication: Proceedings: Symposium on Simulation for Architecture and Urban Design at the 2010 Spring Simulation Multiconference

Year: 2010

The ability of evolutionary algorithms and related search techniques to explore a varied space of solutions with efficiency and often surprising innovation makes them useful tools for design. This typically requires the explicit definition of a goal or objective function and so has been ideally suited to engineering optimisation tasks. For many design problems however, and particularly for those of great complexity, it is difficult to specify such a goal in advance. Design and creativity themselves, particularly in a social context, are often seen as processes of guided, but open exploration. Steels has shown that effective languages can be generated without an external measure of quality by allowing robots to speak and evaluate each other in an environment. Such approaches have been incorporated into genetic algorithms by allowing the objective to change over time.

Author: Sean Hanna

Publication:Proceedings of GECCO '07: Genetic And Evolutionary Computation Conference | full text (PDF)

Year: 2007

Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition.

Author: Sean Hanna

Publication: Artificial Intelligence for Engineering Design, Analysis and Manufacturing archive Volume 21 Issue 4, October 2007 | full text (PDF)

Year: 2007