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Computational Design

In the rapidly evolving landscape of design, technological advancements are driving a profound transformation. The shift from traditional hand-drawn methods to a digital realm, including Building Information Modelling (BIM) and Augmented/Virtual Reality, marks a pivotal moment in the industry’s digital revolution. To stay ahead, we embrace Computational Design—a human-driven, holistic digital design process. This approach seamlessly integrates a spectrum of digital tools, optimising efficiency, increasing productivity, enhancing design flexibility, and minimising errors. At Robert Bird Group, Computational Design is integral across project delivery teams, from concept engineering to BIM and data validation, demonstrating our commitment to leading in engineering services through ongoing training and addressing social and environmental challenges.

Automation

Workflow automation and parametric design are essential Computational Design tools that enable our designers to efficiently navigate project lifecycles, streamline workflows, and ensure precision while adhering to specific project requirements.

  • Parametric Design for Adaptability:
    Modifies inputs for adaptable designs within set constraints, valuable for rapid modelling, prototyping, optioneering, and maintaining associative relationships between design elements.
  • Workflow Automation for Efficiency:
    Uses a range of Computational Design tools to assist with or automate common tasks, reducing manual work and enabling more focus on innovation.
  • Enhanced Creativity, Collaboration and Innovation:
    Empowers designers to explore diverse solutions, fostering creative problem-solving and streamlines collaboration through efficient data exchange, leading to better-informed decisions and optimal design solutions.

Optimisation

In the field of Computational Design, optimisation usually consists of inputs, constraints, and an objective where the purpose is to find the ideal scenario based on the objective that satisfies the given constraints with the given inputs. This may yield in a definitive result or a set of optimal results to choose from. Most optimisations techniques applied in Computational Design can be categorised as either Form Finding/Topology Optimisation, Generative Design or Spatial Optimisation, or a combination thereof.

Form Finding/Topology Optimisation

The process of rationalisation of geometry such that is conforms to, or as close to, a natural real-world state based on constraints and target objective. For example, this might be used for shell structures using relaxation algorithms to relax a base geometry (say a simple planar mesh) that has fixed support points at arbitrary locations so that the resultant geometry, based on given material properties for the mesh, would resemble the state in which it would in the real-world. Another example would be finding the optimal positioning of structural members on a facade based on forces acting on the building mass, this would be a Topology Optimisation exercise.

Generative Design

Generative Design is a method of generating results based on input and constraints. The results are then either hand-picked manually or automated using other optimisation algorithms (such as genetic or spatial algorithms) and can be fed back into the generative algorithm where the results will begin to converge on what has been previously selected. This is used widely for architectural concept design, additive/subtractive design, optioneering etc and commonly uses Machine Learning and AI to inform design decisions.

Spatial Optimisation

Spatial Optimisation is another field of optimisation within Spatial Analysis that has a wide array of use cases within Computational Design. This could be used for urban planning, egress/shortest route analysis, spatial ordering/searching optimisation etc and is commonly used with other optimisation techniques.