مجالات التصميم الحسابي في الهندسة المعمارية

It’s time to say goodbye to traditional drafting tools because computational design methods have arrived on the architecture scene.

Thanks to technological advancements, the AECO industry is revolutionizing everything from design to construction with the help of futuristic tools.

Computational design techniques process data using a set of parameters and algorithms to address design challenges.

While working on a computer, every detail of a design is translated into a coded computer language to create models and analyze the design.

Once data is entered or inputs are provided, the machine can repeat this process several times to obtain the desired results.

Software such as Rhinoceros 3D and Grasshopper are commonly used in computational design around the world.

Besides, software such as Siemens NC, CATIA, Dynamo, Marionette,

Param-O, Vizpro and Fusion 360 are also used to create computational drawings.

Benefits of computational design for architects

Computational design helps save time, effort and resources invested in manual or CAD-assisted drafting.

It also provides architects with the freedom to experiment with multiple shapes and sizes by making simple changes in the algorithm.

This ability to experiment helps mitigate design risks.

Creating error-free designs also helps keep project costs under control.

Moreover, repetitive tasks can be automated using computational design tools.

Once a task is entered, the algorithm can repeat it multiple times to achieve the desired output.

Types of computational design in architecture

Computational design methods do not require architects to learn textual coding.

Most of these tools run on visual programming that allows architects to develop their designs graphically.

Here are the key sectors of computational design that are reshaping the AECO industry.

 

Areas of computational design in architecture

 

Parametric design

Parametric design is an area of computational design that uses algorithmic processes to develop parameters that determine a building’s design.

The origin of the term “parametric” is a word that means a property that helps define a system.

Parametric modeling can be classified into two categories based on its results:

  • Diffusion-based systems: Algorithms develop the final structural form based on initial parametric inputs.
  • Constraint systems: The structural form is first finalized based on the evolution of the algorithm set.

 

Technology: Parametric architecture, a valuable innovation

What are the benefits of parametric design engineering?

  • Create dynamic forms: Parametric design benefits architects by allowing them to explore unusual built forms.
  • Structural Stability Mapping: Performing various computer trials and errors helps address realistic structural hazards, and this leads to higher design quality and better productivity.
  • Increased speed: Parametric design provides speed of reaction and flexibility, and complex built shapes can be created through simplified geometry.

 

Areas of computational design in architecture

Algorithmic design

Algorithmic design is a subset of computational design that relies on a logical set of rules and programs.

The idea is to create an output that incorporates the computational complexity of design visualization and expression.

This helps architects evaluate, analyze and iterate their designs through a specific methodology.

Algorithms allow designers to achieve better aesthetic quality,

structural performance and sustainability while automating all labor-intensive tasks.

Benefits of algorithmic design

  • Save resources: Similar to parametric design, algorithmic design reduces resource investment and helps streamline project workflow.
  • Exploring creativity: Algorithmic design allows architects to unleash their creative potential and create dynamic, free-flowing structural forms.
  • Reduce scope for error: Using algorithms helps in creating error-free designs by testing multiple permutations and combinations.

 

Areas of computational design in architecture

Generative design

Generative design uses algorithms to create a number of design options that architects and designers can evaluate.

It is an iterative design process that relies on user-specified inputs to generate multiple design ideas that help achieve the desired outputs.

In generative design, the designer can also define success metrics to help evaluate results.

Digital tools such as artificial intelligence and cloud computing also help generate a large number of ideas that are classified according to user-defined metrics.

 

Areas of computational design in architecture

 

Biomimetic design

The term biomimicry has its origins in the Greek words “bios,” meaning “life,” and “mimesis,” meaning “imitation.”

Therefore, as the name suggests, biomimicry refers to imitating naturally occurring designs and strategies to devise sustainable building solutions.

Biomimetic design is a branch of architecture that seeks inspiration from nature to design structural forms.

 

 

Digital manufacturing

Digital fabrication is a manufacturing and construction process that uses a machine to build a structure.

This construction method is gaining popularity among architects, product designers,

and furniture designers because it provides the opportunity to create complex designs with simple geometric formations.

3D printing is a digital manufacturing technology that is now widely used to construct residential buildings.

 

 

Machine learning

Machine learning and artificial intelligence (AI) help architects unleash the next level of creative thinking.

While machine learning gives computers the ability to learn without any specific programming,

artificial intelligence helps them perform tasks that normally require human intelligence.

These tools also rely on vast amounts of architectural data to provide insights and automate repetitive design tasks.

Machine learning process

Machine learning is a technology that works on data received from simulating the processes of human intelligence.

The system processes a large amount of data to set correlations to make design predictions. Its programming is focused on 3 aspects:

  • Learning: Obtaining data and turning it into clear, actionable information
  • Logic: Choosing algorithms to generate the desired output
  • Self-correcting: adjusting algorithms to ensure appropriate results

 

Next Generation Civilization Kit 2.0 – Shea’s Parametric Ideation Competition 2020.

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