Data + Design = Decision
May 11, 2022
Our experts are delivering a custom approach to computational design to achieve informed data-rich design solutions for each client¡¯s needs
Data is a huge contributor to the design process and embracing data in new ways is allowing us to engage our clients more intimately in the design decision-making process.
While many clients share common problems, each has their own unique challenges in every project. But what is the best way to address these in a timely and cost efficient manner? That¡¯s where computational design comes into play.
Weaving technology into our work through computational design allows us to utilize technology to target specific project issues and client needs, delivering a unique solution for each design issue. By pushing boundaries and creating new ways to tackle design problems, we have recently developed solutions for mixed-use projects, single building form generator, Life Cycle Assessments (LCA), and more.
For example, we recently concluded a healthcare master planning project to assess the client¡¯s current space and possible expansion options. Utilizing artificial intelligence (AI), we created a custom code to automate the 3D modeling of their existing buildings, which house over 300 departments, as well as multiple design options. Through this process we were able to discover missing information and gaps that may have been overlooked in the traditional design process¡ªnot to mention it was completed in a fraction of the time!
Weaving technology into our work through computational design allows us to utilize technology to target specific project issues and client needs, delivering a unique solution for each design issue.
Bridging the gap for infrastructure projects
Although computational design has had exposure and time to mature within the architecture industry, it¡¯s still relatively new for infrastructure projects. Still, it is easily evident that this approach to solving design challenges will revolutionize how engineers worldwide tackle design challenges both great and small.
Within Â鶹´«Ã½¡¯s Infrastructure business, we¡¯ve been able to automate workflows that require both a rule-based and iterative design process using computational design. For example, we developed a tool to automate the design of custom turning chambers for sewers in accordance with the procedures and requirements of the New York City Department of Environmental Protection (DEP).
By looking critically at the problem-solving process rather than the problem itself, we gain a greater insight into how to solve even the most complex design challenges.
Prior to implementing this tool, designing a turning chamber followed the criteria set forth in a NYCDEP Design Directive by using Microsoft Excel to calculate several chamber parameter values and then using AutoCAD to produce a 2D representation of the resulting data. Any changes to the proposed design throughout the project lifecycle would then require repeating all the prior steps¡ªclearly very time-consuming.
The new workflow allows an engineer to create an information model representing their proposed design and then use a computational design tool to create an in-situ 3D model of the proposed chamber that meets the minimum requirements set by NYCDEP in real-time. Additional parameters integrated into the computational design solution allow an engineer to modify the default design to meet specific criteria for the chamber, site, or project.
While this example focuses on a very specific and clearly defined workflow, using computational design also allows our engineers to have a more holistic understanding of how to approach and solve more complex and unconstrained design challenges.
Data is a huge contributor to the design process and embracing data in new ways is allowing us to engage our clients more intimately in the design decision making process.
More informed decisions early on
Complex design tasks often require understanding not only how individual components are dependent on their relationships with existing conditions but also recognizing how adjacent components within a system affect one another and collectively contribute to the overall final design.
By using computational design workflows to visualize these constraints and relationships, our engineers can more quickly iterate through design alternatives to arrive at an optimal solution unrestricted by current limitations in our design authoring platforms.
Let¡¯s take a panelized retaining wall for example. The design requires balancing several criteria ranging from¡ªbut not limited to¡ªthe wall¡¯s location, how much soil is being retained, how often the wall should be stepped to reduce the number of unique wall panels, to the various types of panels, supports, and access points. By developing a computational workflow that creates an information model accounting for all these criteria, we can make more informed and impactful decisions early in the design process.
Computational design provides a means for our engineers and designers to solidify the logic used to solve various design challenges, allowing each step in that logical progression to be examined and optimized. And by looking critically at the problem-solving process rather than the problem itself, we gain a greater insight into how to solve even the most complex design challenges.