Computational design: it is not just for the complex stuff

Author: Emily Scoones

Date published

20 August 2025

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Computational design: it is not just for the complex stuff

Blog

Author

Emily Scoones

Date published

20 August 2025

Author

Emily Scoones

In this blog Emily Scoones explains computational design and how it can help structural engineers.

What is Computational design?
Computational design combines design thinking with computational methods, algorithms, artificial intelligence, and digital tools to create efficiencies in design and create innovative solutions to more complex challenges. This approach not only accelerates the design process but also opens up new possibilities for creativity and optimisation for not only the design or project but also engineers themselves.

Computational design has been transforming the Architecture, Engineering and Construction (AEC) industry for many years, from Gaudi’s Hanging Chain Models to Daniel Piker’s Kangeroo Physics Engine. It has enabled developments in academia and industry to create beautiful complex geometry, to form-find efficient shapes to minimise material use and building forms to optimise for performance. By integrating computational methods with traditional design principles, engineers can achieve more efficient, precise, and adaptable structures.

Computational design has developed a bit of a reputation for applications on complex geometry or flag-ship projects and that a specialist skillset is required to use it. However it has the ability to be applied more widely, even just in small ways, to have an impact on a projects and the industry.

How can it help structural engineers?
Structural engineers are often faced with changing designs, interfaces with multiple disciplines and often conflicting parameters, alongside project time pressures and a vision to create more sustainable designs. Computational design tools can support this process by automating repetitive tasks, increasing collaboration, and enabling more sophisticated analysis or wider exploration of the solution space. Here's how:

  1. Increased efficiency: Computational tools can rapidly generate and analyse multiple design iterations, helping engineers find optimal solutions faster than traditional methods.

  2. Enhanced accuracy: By harnessing the power of algorithms and precise data modelling, computational design reduces the risk of errors and enhances the reliability of structural assessments.

  3. Optimised design: With computational methods, engineers can explore innovative configurations and materials, pushing the boundaries of traditional design to achieve more sustainable and cost-effective structures.

  4. Collaborative workflows: Digital and computational platforms facilitate collaboration among multidisciplinary teams, fostering integrated workflows and shared insights.

  5. Wider insights: By increasing collaboration and the ability to embed more information into one place, computational methods enable the development more holistic solutions and gain wider insights for the project but also for individuals.


How to take the leap?
Engineers have a natural talent for computational design due to their well-practiced solutions-focus and logic skillsets. The basic principles of algorithmic design are most likely already being applied through the use of formulas in calculation spreadsheets.

The next step is identifying opportunities within projects to apply digital and computational methods, which involves understanding the specific challenges these projects present. The real key is to embrace a mindset geared towards rapid learning, including accepting potential failures in a safe computational space. By adopting this approach, engineers can truly leverage these powerful tools.

Emily was the keynote speaker atthe Digital and Computational Design workshop.

Emily is the Head of Digital & Innovation for Ramboll Buildings UK and a Chartered Structural Engineer. She is passionate about using technology to aid better collaboration and decision making to develop better outcomes for projects.

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