Susan Smith has worked as an editor and writer in the technology industry for over 16 years. As an editor she has been responsible for the launch of a number of technology trade publications, both in print and online. Currently, Susan is the Editor of GISCafe and AECCafe, as well as those sites’ … More »
Technologically Enabled Design and Assessment of Urban Form
March 20th, 2012 by Susan Smith
Keith Besserud, AIA, is the director of BlackBox, a research-oriented computational design resource within the Chicago office of Skidmore Owings & Merrill (SOM). With design partner, Ross Wimer, Keith set up the BlackBox studio in 2007 to lead the development and integration of advanced computational concepts within the multi-disciplinary design processes of the office. This includes reviewing computational tools used in architecture and how they apply in urban design.
The group is interested in exploiting various types and sources of data to guide form-finding design processes. Within this approach the group relies on parametric frameworks built with scripting expertise and parametric software, as well as a variety of simulation and search optimization tools, including those that are commercially available as well as those that are custom-developed by the team.
Keith Besserud works at SOM in the Chicago office. “We do a lot of super tall architectural work. These types of buildings require a tremendous amount of innovation that has spurred a lot of computational work, especially from a structural engineering perspective, how do you actually execute these kinds of projects. There is a strong tradition of using computational form that goes a long way back, and leveraging those kinds of tools as part of the design process.”
The company has a significant urban design practice, which in Besserud’s terms is nowhere near as sophisticated in terms of the use of the computational tools in the architectural world.” One of the missions of the architectural group is to try to introduce the designers to the power of computational tools and the attendant perspective they bring with it.
Besserud outlined five different domains of the influence of computational tools.
“The reason that parametric geometry is important, in my opinion, is simply that it enables much more efficient iteration, which is a fundamental part of a design process.If you have parametric geometry, if you have the ability to command it at a parametric level…you have the ability to iterate much more quickly through different design iterations.” Many tools in the architectural world that are commonplace today have a parametric framework to them.
The urban design group is still working primarily in tools like AutoCAD, which is not a parametric environment.
“What that means is that every time there’s a change being proposed to a design that wants to be evaluated, there’s a very laborious process of having to redevelop that model from scratch, or at least the pieces that are going to be changing,” said Besserud.
A few years ago SOM were using a piece of software that called Digital Project built on CATIA, a very strong parametric environment. They demonstrated what happens when you create a rules-based model environment and can move things around and have things update on the fly and repair themselves, and also be able to keep track of all the quantities and have instant feedback on all that information.
Although SOM hasn’t taken a close look at CityEngine yet, Besserud said they definitely will be, because that same type of power is built into it.
When you become fluent in the use of computation as the analytic portion of design, you have the ability to generate all these parametric variations very rapidly. “You need to have some way of being able to evaluate them, so we are very interested in looking at analytical tools, developing them there where we need to, and using commercial tools where they are available,” said Besserud.
Analysis of View Considerations
One area SOM is interested in is the analysis of view considerations, a type for measurement. The question of view quality comes up: what is a good view versus a bad view? This is a difficult thing to quantify, and working with ArcGIS Esri started to look at a project that SOM was involved with for a redevelopment project in China, which involved midscale development activity and an iconic high-rise piece.
“We were interested in beginning to try to understand what happens when you start moving the forms of the buildings around, reshaping the overall skyline, the placement of the midrise buildings, and so forth,” said Besserud. “In terms of their ability to see the iconic tower, in terms of all of the buildings’ ability to see other landmarks that are in the vicinity, and for the tall tower, in particular to be able to see different things in the region. There is a process of discretizing the geometry into a series of points that become the evaluation nodes, then a process of basically rate tracing to connect point to point and determine if there’s interference or not, then to somehow summarize that, and add it up, and turn that into kind of a heat map that gives you some information about a more intuitive way of where you have issues related to views.
The View Feature was developed in Black Box for view analysis.
SOM had developed their own view analysis tool some years ago, a plug-in to Rhino from Robert McNeel & Associates. Rhino is more pixel-based in its orientation, and had the ability to provide evaluation and internal ray tracing, then creating a user interface to control the view cone.
SOM developed their own genetic algorithm some years ago, as commercial tools weren’t available to serve as optimization engines. A genetic algorithm is something SOM created to design windows for a military academy in Kuwait which they needed to minimize direct solar gains through the windows. They started with a basic shape that was the window opening itself which had a diamond shaped shroud that came from that surface to provide shading. This “automated optimization exercise” required them to identify a parametric control mechanism. In a genetic algorithm it generates a random population of window geometries, or “genomes” which are different window shapes. They then automated a process of sending each one of those into a piece of evaluation software.
There are other criteria to add into it like constructability and cost. This solution could be used at the urban scale, although it hasn’t been tried yet. With this approach, a building’s shape can be built in order to maximize the amount of incident solar radiation, which would help accelerate building designer intuition, according to Besserud.
Semantic modeling at the urban scale
Esri’s tools enable the construction of semantic modeling. “We could use databases that are already set up to enable semantic intelligence, but we’re going to go through the process of trying to do that ourselves from scratch,” said Besserud. “What does that really mean to have a semantic model? How does a computer know that a building is a building? And what does that really mean?” and “how do you build the relationships between all those other things within the environment of a database?”
“In learning about the behavior of cities we need to integrate buildings as objects within a much larger system and connect all the systems together,” Besserud said, adding, this is an “enormously ambitious project.”
Urban design can begin to think about the way that Esri is using background imagery for some of the analytical tools that they’ve developed. Besserud said it is possible to create a system of rules that might begin to lay out some logic for the development of the design of an entire city. This would be agent-based modeling at the urban scale.
“When Black Box got started we were purely an overhead research group, and so there was a lot of this type of research conducted in the 2008-2009 time frame,” said Besserud. “When the downturn hit, we had to become much more billable. So everybody became embedded in teams working on real projects.” This situation created a double-edged sword, “On the one hand, we became much more intimately integrated with the actual project work going on…on the flip side, we’ve had to postpone further development of a lot of this type of research work, but with the semantic model we are moving back into more of that speculative research work.”
One of the interesting things between optimization exercises as they are now conducted and these kinds of agent-based interrogations where an agent-based environment is looking for stability, is that optimization exercises are looking for a super freak – that stands out from the crowd completely and is not worried about balance, according to Besserud.