Advanced Architecture Design Portfolio_2018-2021

Suhandhan Ravee Advanced Architecture Design Portfolio

Staedelschule (SAC) || Master Thesis || Collaborative Works || Personal Speculations 2018-2021

Curriculum Vitae

Suhandhan Ravee suhandhan.ravee@gmail.com +49 176 58834118 Frankfurt Am Main, Germany Architektur Portfolio Link here

Education 2018 - 2020

Master of Arts in Architecture M.A. Architecture Advanced Architectural Design

2013 - 2018

Bacheclor in Architecture B. Architecture

Städelschule Architecture Class Frankfurt, Germany

Anna University Chennai, Indian

Work Experience 11.2020 - 05.2021

Architect

12.2016 - 04.2017

Intern Architect

07.2016 - 11.2016

Intern Architect

06.2015

Summer Intern

PAU-a GmbH @ Wiesbaden LPH 1 - 5 (Design Team) DesignQube @ Bengaluru Design Team and Working Drawing DesignQube Interiors @ Chennai Design Team and Client Handling DesignQube Interiors @ Chennai Working Drawing

Skill Sets VR / AR / XR

Photogrammetry

Environment’s Designing

Media Artist

AI + Architecture

Interactive Spatial Exhibitions

AR Artworks

Graphics Design

GAN’s & CNN’s

UX & UI Designing

Game Design - Unity Engine Application Designing

Software Proficiency Unity C # + ML-Agents Python

Rhino + V-ray Grasshopper Sketchup + Vray

Maya + MEL

Z-Brush

Processing

Lumion

Jupyter Notebook

Keyshot

TouchDesigner VVVV

Adobe Suite

ArchiCAD Revit AutoCAD

Language English German

Full Work Proficiency Limited Work Proficiency

Experience Objects, Groups and Couplings SAC Workshop SEM-1 || Photogrammetry + Rhino + Keyshot + Illustrator Workshop towards the contemporary discourse on repurposing ‘the real’ in art and architecture through adhocism and spolia using digital tools. Using Agisoft to build a digital twin of the objects group and manipulating with other digital softwares.

Leaky Stuff SAC EOS Project || Photogrammetry + Zbrush + Keyshot Speculation towards an “Architectural Leak” using digital tools like Photogrammetry, CNN’s, GAN’s, Deep Dream AI etc. A leak, to oppress the binary opposition between different representations by being inclusive of their political and economic constructs.

Machine Vision Research Segment - 1 || Processing + Kinect Camera Using processing scripting language, extracted raw depth data from xbox kinect camera to build a Real-time point cloud of an observed environment containing an interactive behavior of the observed subject and object.

Future Frame Prediction Model Research Segment - 1 || Python + GAN Using Python scripting, modified an unsupervised Generative Adverserial Neural Network to function as a Future Prediction Machine learning model and predict the future frames of the human skeletal-node video sequence obtained from Kinect.

Visualisation Machine Intelligence Research Segment - 2 || Data Visualisation + TSNE The frames from recordings of previous pointcloud experiments serve as datasets for the training of Convulational Neural Networks. These high-dimensional Datasets are visualised by reducing its dimensions to 3D or 2D using open T-SNE.

Responsive Environment Research Segment - 2 || Touchdesigner + Kinect Camera Utilising Touchdesigner, developed a Real-Time Responsive environment as part of ‘Rundgang’, an open house exhibition in SAC. With the help of kinect camera observing the corner condition, a lite responsive environment was exhibited.

Immersive Behavioural Envrionment Master Thesis || Unity + C# + ML-Agents A Design tool/ Game environment was developed in Unity Engine + C# by utilising Unity’s ML-Agents and training it with Reinforcement learning and Behavioral learning methods over the virtual environment and user’s spatial and interactive behaviour.

Figures and Misfits Collaborative Project || Unity + C# + AR Foundation A collaboration as an AR app developer for the production of a library of Phygital forms (Physical + Digital). The app enables digital figures, produced through photogrammetry, to be augmented over their own physical self and other figures for analysis of fit.

Objects, Groups and Couplings SAC First Semester Workshop Softwares: Rhino, Agisoft, Illustrator & Keyshot

Collection of Objects

This workshop takes its cues from contemporary discourse on repurposing ’the real’ as well as from historical examples of re-purposing in art and architecture, such as the found object, adhocism, and spolia.

We started working with the umbrella theme of ‘Natural-organic’ objects. The curation resulted in a top viewed 3D massing that resembled architectural structure, contradicting the history of the objects that forms the massing and their base character. This helped question the visual play that human perception and also speculate on the digital understanding over this group. Photogrammetry was used to produce a digital model which was tweaked and manipulated with the extents and limitations of the software interpretation of the objects.

Grouping of Objects

White-light

White-light with shadows

RGB light

Coupling Iterations

Leaky Stuff SAC End of First Semester Project Softwares: Rhino, Agisoft, Maya, Zbrush, Keyshot & Photoshop

A “Leak” is defined as the slippage that happens when multiple realms of representation try to coexist in a single image or an architectural space. Leakiness transgresses political and economic constructs by being inclusive. It embodies and delivers an aesthetic of fluidity and is usually specific to contemporary technology.

Leakiness sees different regimes of aesthetics coexist and cancels out the previous binary opposition between different representational regimes that previously existed in sequential exclusiveness.

Style GAN Output overlayed over the Content Image

Style GAN Output overlayed over the Content Image

Style images with the use of Deep Dream AI application (A style transfer GAN AI) is let to “Dream” on the content images. A stylized output with essence of both the Architectural content image and themed style image is extracted and analysed for a “Leak” through mediums.

Style GAN Output overlayed over the Content Image

Renders showcasing the “Leak”

The second part of the brief was to produce a similar repercussion of “Leak” but in a spatial extent with the subject being breached, taken from the context of Frankfurt city. While on the process of photogrammetric documentation of the chosen architectural facade, there was a surprising revelation created amidst medium transfer which became the catalyst to thrust my design approach forward.

Due to snowfall, during the processes of image capturing, the digital mesh thus created through photogrammetry, had the impulse of snow over the facade elements. This hoaxed the software to misinterpret the settled snow layer as extensions of the building itself. I read this phenomenon as a leakage of natural element into an architectural artefact through the digital tools of modern architecture.

Overlapped frames with skeletal nodes from Kinect camera

Machine Vision Research Segment 1 - Master Thesis Softwares: Processing, Java Scripting & Premier Pro. Using Kinect camera, the machine observes the interaction of the subject with its environment.

The dataset thus produced is in-turn viewed and processed by a human subject creating a second order Cybernetic system. A Feedback loop emerges

Overlapped frames with skeletal nodes from Kinect camera

Sequence of frames from machine vision over the environment

Responsive Environment Research Segment 2 - Master Thesis Softwares: Touch Designer, Processing & Python

As a part of ‘Rundgang’ @ SAC, an installation was developed equipped with an responsive machine creating an behavioral responsive environment. The machine overlooked and mapped a corner condition.

In response to the magnitude of activity, proximity of activity and quantity of activities, it disintegrated or built the mapping of the corner condition.

Construction and Disintegration of the environment in virtual space

1 - Fully Constructed

2 - Lossing Clarity

3 - Lost Identity

Loosing Stability - 4

Towards Disintegration - 5

Fully Disintegrated - 6

Visualising Machine Intelligence Research Segment 3 - Master Thesis Softwares: Jupyter Notebook, Python & Artificial Intelligence Models

Sample Frame in Dataset

Frame overlayed with skeletal nodes

Part of Training dataset

Post installation, the datasets obtained from the machine vision veiwing the subject environment was compiled and fed into a Generative Adversarial Neural Network(AI Model - GAN). Primary intentions of the research were to use the GAN model in ‘Future Frame Prediction’ with a sequence of frames as training data. Later the curiosity tilted over visualizing these massive training datasets stored within these AI Models in 2D and 3D virtual space.

The AI Model processes the pointcloud datasets and based on certain set parameters stores the points within its memory space. This memory is tapped into and built into 2D and 3D space, but the building criteria is from the parameters of storage of the AI model and not the criteria’s of physical realm.

6 Sets of Overlayered Datasets - each dataset contains over 5000 points

12 Sets of Overlayered Datasets - each dataset contains over 5000 points

In case of the 2D visualization, the python script reaches out to the AI models memory space and calls out each layers of training datasets processed and stored.

Then these Binary Dataset is built into a 2 dimensional figure using the parameters stored within each data as the structure.

While building a 2D visulaization, each dataset is retreived separately.

Hence the distingushable layers in the visualiation.

When the 3D visualization was began, the limitation in the python code to callout each dataset separately was cured, resulting in the entire training dataset interacting with one another in the virtual representational space. The visualization was repeated with increasing the number of datasets being visualized.

The 2D and 3D ouputs produced forms and figures that parametric in nature and mathemetically reinforced. The forms could be a discourse for future architectural speculations.

Visualizing 1 Dataset - 5000 points

Visualizing 2 Dataset - 10000 points

Visualizing 5 Dataset - 25000 points

Visualizing 10 Dataset - 50000 points

Mode choice UI

Introducing agents into the work space

Manualy introduing required agents

Immersive Behavioral Environment Master Thesis - Developing a Design Tool Softwares: Artificial Intelligence Models, Unity + Csharp & Machine learning.

Space choice UI

Generating Preset layout iterations

Manualy introduing required agents

Through the different research stages, the understanding of Man-machine communication increased and with it the curiosity of their capabilities. A speculation raised for cohabitation of Man-Machine in an environment and with this emerged the proposal for a Design tool development which enables a platform/virtual environment/home for man and machine to cohabitate and design in it. Such an environment would liberate us from the prejudices of spatial designing and negate the limitations of the physical realm.

The Designland at its core is bifurcated into two modes, with one being closer to our realities design limitations. The Classic mode, when chosen, takes you to an user interface where a space of choice could be designed with assistance from a machine which not only overlooks the agents in the space but also the behavioural patterns of the user/ designer. All the objects in space, by being aware of themselves in the design space and the presence of neighbouring objects attain a certain level of agency.

The machine enables the user/designer to either choose from a series of preset’s trained already my the user with the agents in the space or manual design the space using the available agents.

The design limitations present in the real world are present here. This restricts to a very banal 2D design possibilities.

All the behaviours developed are recorded and the machine trains on it for the future iterations and generations.

Classic mode - work interface and available options

Rewarding the behavior positive or negative

Training of the ML agents over the behaviour/s

Registered parameter - P

Position

The manual designing interface for the classic and the editor mode has certain parametric control differences. In the classic mode interface, the only two possible control by the user over the agents are the position control and the orientation control, similar to real world controls.

Editor mode - work interface and available options

Registered parameter - Relationship

Generating iteration bz the trained machine

Contrastingly, in the editor mode, in addition to the position and orientation, scaling control is added. Also the control of artificial gravity and the rigidity of these agents causing collision with other agents can also be controlled. These parametric controls in the virtual space provides a platform for speculation on how these strong natural characteristics would affect the designing possibilities in their absence.

Training the machine over the behaviour of the user/designer is present in both the modes. The position and orientation of the agents in the virtual space, the relationship of an individual agent to the other agents in proximity and the interaction behaviour of the user/designer over the agents are the three core parameters that the machine observes for the purpose of training. In addition to this, in editor mode a possibility to either reward or penalize certain behaviour is available. This option helps in training the machine on only positively rewarded behaviours which in-turn makes them more user authentic and efficient. All the agents in space become machine learning objects and contribute to the whole.

Iteration - 1

Iteration - 2

Iteration - 3

The machine trained over the behaviour in the classic mode results in generating behaviours similar to the real world designs.

Interestingly, the machine trained over the editor mode, due the multitude of possibilities and opening to the fullest of the 3rd dimension generates outputs satisfying the behavioural parameters but being surreal to the eyes prejudiced by the present architecture reality. This environment attains a man-machine cohabitation taking the discourse into a Neo-Anthroposcene.

Figures and Misfits Collaborative Project || AR Application Softwares: Unity + C# + AR Foundation

Physical Figure 01

Digital Figure 01

Physical Figure 02

Digital Figure 02

Physical Figure 03

Digital Figure 03

AR setup with figures of experimentation

An collaborative project to assist a colleague with the development of an AR application to execute an experiment as part of their project “Poche as Possibility”. The project explores on design possibilities on Poche and borrows Kivi Sotamaa’s ‘Design of the Misfit’, to produce intimate friction of physical figures, from their preliminary experiments, with their digital self’s. A phygital output was previewed as target output and that became the goal of this collaborative project. An AR application was submitted which enabled the above described interrelation through augmentation.

A library of Misfits was inturn produced through this Application wi the same or different physical figures. These augmentations where then through axial rotations with the help of the User Interface input buttons. T the library and later boolean’ed following the technique of Kivi Sotaa design possibilities through Poche spaces.

ith iterative behaviour of augmentation of different digital figures over n analyzed for their fit with the adjustments made to their orientations These iterations were then captured(through the interface) to be stacked in ama. These outputs, later aided in the exploration of the discourse of

AR Apllication main interface

Library of physical figures(grey) augmented with digital figures(pink)

AR Application Augmenting figures

Augmentation captured for further experimentation

Library of Boolean’ed figures of misfit