Computational design
Portfolio Melika Saadi
About me: Hi, my name is Melika Saadi. I'm an architect, computational desirer and eager to research about computer capabilities in digital architecture, specifically the application of artificial intelligence algorithms and machine learning in AEC.
Academic Background Study: Architectural Technology- Master Date: October 2019- May 2022 Place: Iran University of Science and Technology
Study: Architectural Engineering- Bachelor Date: October 2014- December2018 Place: Islamic Azad University of Central Tehran Branch– Iran
Personal Information:
Gender: Female Date of birth: 09-19-1996 Place of birth: Tehran - Iran Nationality: Persian E-Mail: melikasaadi96@gmail.com Tel: +989367649536 Instagram: @melika_saadi Linkedin: www.linkedin.com/in/melika-saadi Youtube:https://www.youtube.com/channel/UCT QtOUyILmIkuDz-Je7UlvA
Study : Mathematics Physics - Pre-university Date: October 2013-june 2014 Place: Tehran-Iran
Study : Mathematics Physics- Diploma Date: October2010-june2013 Place: Tehran-Iran
Software Skills:
Extracurricular Activities
Rhino
Activity: Date: Institution :
AutoCAD training course September 2015 Iranian architecture center
Activity: Date: Institution:
3dmax –vray training course September 2016 Iranian architecture center
Activity: Date: Institution:
Post production training course September 2016 Iranian architecture center
Activity: Date:
Holding an architectural exhibition November 2016
Activity: Date:
Grasshopper training course November 2020
Activity: Date:
Python (programing language) training course January 2021
Activity: Date: Institution:
AI (Machin learning- deep learning) course June 2021 Coursera
Grasshopper Python (programing language) Revit
Key shot (rendering) Enscape (rendering) Adobe Photoshop Wondershare filmora AutoCAD
Sketch up Ms office Other skills: Fast typing
Music performance (violin) Languages: Persian (native) English
. . . . . . . . . . . . . . . . . . . . . . . . .
Contents
. Cellular Automata . 16 . Housing Automata . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Fabrication Minimal Surface Swarm Intelligence
. . . . . Deployable modular unit 7 11
Kinetic Architecture 18 Space layout optimization
. . Automated space layout . (theses) 22 . .
. . Parametric Design-Coding . Personal experiences 34 . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
GANs 2D to 3D Application of AI in generating 3D forms
32
Digital Fabrication Minimal Surface
In this university course that was attended in the fall of 2019, the primary challenge was to find a solution for the fabrication of the chosen minimal surface. The chen–gackstatter surface family is a family of minimal surfaces that generalize the Enneper surface by adding handles, giving it nonzero topological genus. They are not embedded, and have enneper-like ends.
6
This form was modeled in Rhinoceros and Grasshopper. In Grasshopper we made the strips from the 3D model's surface and then unrolled the strips. In the fabrication process, we encountered some challenges, to overcome them we used some experimental methods. One of the challenges was that we weren't able to have a boundary, therefore having anchor points for the strips was impossible. So we decided to divide the surface into 8 parts and make each part separately, then joined the separated parts of the surface together. 7
8
In the final method, we used rivets as connectors for the strips. Furthermore, the chosen material was cork, it is flexible, elastic, and strong enough in case of internal forces. in another hand, it can be cut by CNC machines easily.
Video clip of this project is available in this link.
9
Digital Fabrication Swarm intelligence
In this university course, at first, it was attempted to create a creative form using swarm intelligence algorithms by means of Colebra plugin in Grasshopper. Following it, putting the fabricated strategy into action was the biggest issue. The purpose of this construction challenge was to develop a technique to produce a model using the casting and molding technique so that the molds are not discarded after execution and can be reused. 10
According to the form's challenge, a portion of the obtained shape was deemed a sample for implementation. It is worth noting that this procedure may be used to create the complete form.
11
12
Due to the complicated bends and voids in the proposed shape, a single mold could not be formed. As a result, it was divided into smaller pieces that could be implemented and removed from the mold. Each part of them was created in Rhino software using the contouring approach, such that the layers formed a complete model. After the form's various components were made, they were all sanded down and joined together to produce a smooth form.
Video clip of this project is available in this link. 13
Cellular Automata In this course, after recognizing about cellular automata algorithm, we attempted to build various 1D patterns by establishing different initial codes.
14
Furthermore, using the original rules of the game of life, we visualized the 2D CA in three dimensions. Then, using cubic modules, we experimented with various initial codes to get different results in 3D.
15
Housing Automata This code has the capacity to parametrically offer the user options like selecting the block's location on the property, its number of floors, the arrangement of its units, and the format of the facade within the current framework.
This approach was used in a practical project. In this project, an attempt was made to define the rules of proximity of spaces in a residential plan using the format of cellular automata, in order to automate the plan design process. 16
It is possible to automate the design and construction of housing using this design method, despite its limitations caused by the nature of the cellular automata algorithm.
Scan for video clip of this project 17
Deployable modular unit Kinetic Architecture - Space layout optimization
This project's purpose was to design a prefabricated modular unit, which is deployable and can be transferred to any type of project, such as critical settle needs or temporary structures.
18
This module has the ability to be loaded directly from the factory on a Trailer or truck and transported to the desired location for implementation. In this project, we tried to consider the opening and closing mechanism of the module and the design of all executive details and joints.
Scan for video clip
19
20
We practice the allocation of spaces based on their function, using computational tools such as optimization and against-based modeling, to find the desired layout of the units. We consider a therapeutic complex as an example, where the algorithm attempts to produce the desired arrangement based on the defined adjacency matrix while using the identified forces.
Scan for video clip
21
Graph2layout Space layout generation using Deep learning algorithms
In this study, we suggested a novel deep learning approach for supporting designers in collaboratively creating architectural space configuration. This study aims to enable architects to make use of Deep Learning algorithms’ abilities to develop realistic space layouts while maintaining high levels of control over the resulting solutions. The approach involves applying the cGAN algorithm to the graph structure of the input bubble diagram to generate a node-based space generation on a pre-defined footprint. 22
23
The design of an apartment's residential plans was considered in order to implement the algorithm. Two different sites were tested, and layouts were created on each of them, to evaluate the algorithm's capabilities.
It works by requiring the algorithm to receive two conditions: the boundary of each unit and the relationships of spaces and their types as defined by a bubble diagram. 24
25
26
27
28
Scan for video clip
29
GANs 2D to 3D Application of AI in generating 3D forms
This project was the final product of the Digital Futures workshop titled 'Gans 2D to 3D', in which new 3D forms can be produced by using the Gan algorithm. In this method, in order to obtain data, the available volume is cut by contouring method and converted into a series of 2D images. These data are then given to Gan's algorithm as learning data and the algorithm is able to generate new images similar to them. By rearranging this series of images, we get a new three-dimensional form. 30
The technique of texture mapping using style transfer was employed for the material of the object. 31
Parametric Design-Coding Personal experiences
32
33
34
35
Thanks for your attention Contact me via melikasaadi96@gmail.com 36
