Computational design is an advanced approach that utilizes digital tools and software to create, evaluate, and automate various tasks. It uses computer programming and analysis to explore multiple design solutions and optimize the performance of the final design. Rapidly adapting these approaches can help to enhance productivity and design outcomes.
Computational design (Create)
“Design is change. Parametric modelling represents change.” With clearly stated parameters, design can be created, evaluated ,and recreated with ease. However, to create clearly stated parameters, architects must also think about the logical sequence of formulas, parameters, and relationships that explain how to create their designs.
An algorithm is a procedure for addressing a problem in a finite number of steps using well-defined instructions and logical if-then-else operations. Algorithmic design can help to solve design problems that be reduced to well defined rule-based logic, shifting architect from architecture programming to programming architecture.
Generative models create limitless options based on selected parameters to be evaluated by evolutionary or other optimization algorithms.
Analysis (Evaluate)
Parametric designing of structures is not always about creating complex geometry. It’s about generating better solutions. The generative design emerges as a way of designing by stipulating the parameters and restrictions to be met so that the algorithm then delivers different alternative solutions.
Buildings effect and affected by the environment. Environmental analysis helps to design with context, by measuring radiation coming from the sky, analyzing radiation on surfaces causing solar gain, sunlight hours, and the amount of sky blocked by buildings.
Daylight analysis helps to evaluate daylight performance of buildings in terms of sufficient daylight, protection from glare, sun exposure, and outside visibility both annually and point in time and user’s visual comfort status can be improved based on analyses.
Optimization (Decide)
Evolutionary algorithm (EA) is a class of stochastic optimization methods that mimics the process of natural evolution. Evolution is, in effect, a method of searching for “fitting solutions” for complex problems in very large data sets containing limitless possibilities. Evolutionary algorithms help to see the trade offs between the objectives, and make optimal decisions based on the requirements of design, giving a set of optimal solutions set to designer to choose from.
Machine learning – is a branch of artificial intelligence (AI) and computer science, which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Professional Computational design services
It is clear that the benefits of parametric and data-driven design are significant, automating traditionally repetitive workflows and enabling engineers to instead focus more time and attention on delivering added value on a project, whether in terms of design optimisation or a structure’s geometric complexity.
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