encoded memory machine learning workshop with grasshopper
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encoded memory 1.0 _ machine learning Workshop
INTRODUCTION PROGRAM AND OVERVIEW METHODOLOGY SOFTWARE PLATFORM EXPLORATION OF TOOLS GROUP PROJECTS
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encoded memory 1.0 _ machine learning Workshop
MACHINE LEARNING techniques have developed tremendously over the last two decades. We’re currently witnessing the renaissance of the supervised learning, which is directly influencing our lives mostly without our awareness - via the SEARCH ENGINES, recommendations systems, social networks and more. As with any
well-developed technology, we are asking the same question again - how can it INFLUENCE our culture and change our perspective on the surrounding world? How can we ADAPT those tools to be used IN ARCHITECTURE? What could be the benefits for the designers and how can machine learning change the way we work and create?
The premise of a flexible and ever-learning, ever-adapting work environment seems like a viable vision of the FUTURE for the creative industry. To achieve that, we need to know exactly HOW AND WHY machine learning works. Finding a creative application for this set of tools in not an easy task, and requires us to develop a NEW
UNDERSTANDING of the digital modelling concept. Our parametric models have to account both for human and machine readability, to be able to benefit from the two worlds. To make it happen we need to work on the translation between THE ORGANIC AND THE DIGITAL MEMORY - the common encoding.
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encoded memory 1.0 _ machine learning Workshop
Encoded Memory was an
the DESIGN WORKFLOW. ADVANCED GRASSHOPPER Multiple data sets were tested in WORKSHOP that aimed to tandem with different techniques. explore the potential of machine The participants explored learning in the development of such concepts as: regression, an urban fabric of a particular classification, backpropagation, sector in Dubai. The program clustering, autoencoder networks demonstrated how the parametric and more. Based on the neural model is presented to the network INTERPRETATION of NEURAL NETWORKS and the INPUT DATA, participants other MACHINE LEARNING were able to generate multiple TOOLS and how it can influence design proposals, which gave its effectiveness and decisionthem a better understanding of making process in directing DATA INTERPRETATION
PROBLEMS AND DECISION MAKING PROCESS. They were also able to intervene to adjust the data, or the outcome, in order to influence the network future direction. During the five-day workshop, the applications of machine learning tools in multiple roles were investigated - from the
DESIGNER’S ADVISER to the SELF-LEARNING
GENERATIVE METHOD.
Those methods were employed in the decision-making process, learning how they can assist in the exploration of the parametric space. In order to utilize these TOOLS, participants needed to know exactly HOW they WORK. To get familiar with the theory of the new workflow,, they had to closely monitor the learning and decision making processes, and learn how they respond to the given data set.
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RHINOCEROS and GRASSHOPPER were used as
the main software platforms, along with other GH plugins, mainly OWL (machine learning-oriented data processing), ANEMONE (looping the GH’s computation graph ), GHOWL, MESH TOOLS and UTO MESHEDIT. The Owl plugin had its first public appearance and has been used for the generation and training of all the machine
learning tools used during the workshop. The workshop technical tutorials included: __
MACHINE LEARNING CONCEPTS AND NOTIONS
(supervised vs unsupervised learning, neural networks, clustering, autoencoding etc.) __ APPLICATION OF machine learning in expanding and
navigating the design space, generating different possibilities based on the design intent. __ ENGAGING WITH the machine learning tools, and guiding the possible outcomes. __ DATA PREPARATION for machine learning - ways to introduce the parametric model __
ACCORD FRAMEWORK BASICS.
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The first two days were spent EXPLORING the TOOLS and working on selected techniques. During this time, the participants were familiarized with the variety of machine learning tools and the classes of problems those can tackle. A great accent was put to show the IMPORTANCE of the TRAINING DATA and ITS QUALITY, as well as the ever-present design
PARAMETRIZATION PROBLEM. In the following
days, the task was to apply this newly gained knowledge to a fictitious design problem. The imaginary nature of the problem was chosen on purpose, so that the participants were not limited by the practicality of the methods they were exploring. With the current stage of
WORKFLOW solutions or even
naming conventions which could guide the thought process. The learned techniques were then applied in the following three days to develop INDIVIDUAL PROJECTS as proposed by the participants of each group. The design intent was guided MACHINE LEARNING IN away from the production of a ARCHITECTURAL RESEARCH, single outcome, with a deeper this seems a plausible approach, FOCUS on the PROCESS which is further motivated by OF GENERATION and the LACK OF EXISTING EXPLORATION.
The workshop also explored the POTENTIAL AND LIMITATIONS of the current Owl plug-in and provided NEW IDEAS FOR DEVELOPMENT of its next release. This outcome, along with the groups’ projects, brought MACHINE LEARNING discipline closer to the ARCHITECTURAL COMMUNITY, suggested ways for its implementation within the design space, and enabled new potential workflows through the development of the new tools.
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Group Projects
s
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AUTOENCODED POLYHEDRA
DIMENSIONAL GRADIENT
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This project explored machine learning autoencoder algorithm on a building level. Using polyhedral unit, a parametric model was generated, to deliver a number of origami-based compound types. Four (4) of them were selected as an input for the Autoencoder algorithm to create different variations based on the parameters of the four initial ones. Subsequently only one (1) origami type was selected for as the building geometry on site and developed further.
sensebility for the placement of the building, Weaverbird plugin was used to create panel divisions to the surfaces with their respective openings based on the radiation value throughout the year. Finally, Machine Learning KMeans algorithm was used to create clusters of these panels, and to generate twelve (12) iterations of the building with different opening sizes for each month.
The next step included calculation of the radiation values on different surfaces using Grasshopper plugin Ladybug. Dubai climatic data throughout the year generated colored diagrams based on the orientation angle. In order to determine the best orientation of the building on given site, Galapagos plugin set the condition of lowest radiation levels. Having incorporated enviromental
Those were used as inputs in k-means algorithm to create clustered places. The clusters provided visual maps for the relationships between these places and their differentiation. The driver for a new public seating in Dubai was a simple question: “Can a design communicate a link between seemingly distant and different places?� In order to answer this question, the task was split into three steps. 1. Defining and collecting data of selected places in Dubai and their analysis. Data were collected from social media and other internet sources. They offered information about location popularity, security, land-use, number of reviews, and distances from other buildings.
2. The design of public seatings are defined by 12 input parameters, which control the profile and boundary conditions. At the same time, they are used as inputs in a backpropogation supervised machine learning algorithm to generate a variety of seating units. 3. Finally, step 1 and 2 are interwind by creating clusters of seating units based on the previous analysis, thus linking places that are not similar at a first glance.
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DUBAI URBAN INTERVENTION
URBAN FABRIC
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This project analyzed several existing buildings on the site in regard to the relationship between their heights, and the distance to the park, main road, and the river. These distances versus height relationships become an input for Machine Learning applying Supervised Regression Learning process. The output ( a mass / cubic measure ) was then used to propagate an array of buildings that embody similar relationship to the input data. This output Volume
values can also be translated into different floor area and heights. The outcome of this application was to populate the site with different buildings where their volume is related to the distance to the main road, public park and the river, maintaining same relationships of the existing fabric.
Machine Learning was used to determine building volumes based on their proximity to the weighted networks of Roads, Water, and Pedestrians. Taking the relationship between the distance to the weighted network and the existing buildings (brep geometries) volume and orientation as inputs, the system output the volume and orientation of the new propagated buildings based on their location.
The team started introducing the inputs as curves and breps. Nine curves were used to represent the networks on site, and 21 breps to represent existing buildings with their volume determined by their proximity to these network. The neural network studied the relationship between the distances and the brep volumes and output the volume and orientation of the new buildings to propagate on the designated field.
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GROUP MEMBERS Omar Kaddourah Monika Kalinowska Abdullah Tahseen
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WORKFLOW
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Parametric model
Autoencoder
Orientation Analysis
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CLustering
Result
Sun Analysis
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01 PARAMETRIC MODEL
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02 AUTOENCODER output AutoEncoded
input
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03 ORIENTATION ANALYSIS
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04 SUN ANALYSIS 15th March
15th July
open
15th December
semi-open
closed
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05 CLUSTERING january
february
march
april
may
june
july
august
september
october
november
december
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06 RESULT / RENDERS
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GROUP MEMBERS Khaleefa al Hemli Marta Krivosheek Hayder Mahdi Tang Li Qun
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WORKFLOW
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Collect Dubai Google Data & Define Parameters
Organize & Vizualize Data
k-means
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__location __land use __popularity __reviews __security __distance from other buildings
vizualize Clusters
Bench Parametric Model (12 dimensions)
Backpropagation + CLustering
se cu rit y
ou tdo ors
are a
of the pla ce
ind oo rs /
siz e/
of rev iew s
po pu lar ity rat ing
nu mb er
gra ph ba se
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01 & 02 COLLECT / ORGANIZE & VIZUALIZE DATA
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03 K-MEANS _ RELATIONSHIPS BETWEEN LOCATIONS
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04 VIZUALIZE CLUSTERS / LOCATION RELATIONSHIPS PROJECTED ON THE MAP OF DUBAI
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encoded memory 1.0 _ machine learning Workshop
05 BENCH PARAMETRIC MODEL (12 DIMANSIONAL TENSOR) Bench models consiting of 12 (twelve) parameters forms an input for supervised machine learning. Both input designs are built from the same type of parameters with different values representing boundry conditions. The 12 parameters are : 1. inner diameter 2. open / closed circle for seating (controlled by 2 parameters) 3. continuity / fractioning (number of divisions within the bench) 4. height of the seat 5. depth of the seat 6. round / sharp edges of the seat 7. thickness of the backrest 8. backrest height 9. positioning of the backrest towards inner or outer radius (controlled by 2 parameters) 10. circular / polygonal shape controlled by sectioning “resolution�
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input geometry perspectives
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06 BACKPROPAGATION & CLUSTERING
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GROUP MEMBERS Lina Ahmad Ping Ping Lu Alberto Tono
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ion
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WORKFLOW
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Site Context
Site Constraints as Input Data
Volumne Dimensioning
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Supervised Learning
Result
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01 & 02 SITE CONTEXT AND CONSTRAINTS AS INPUT DATA
This selected site of 1 sq km in size is located in Dubai, UAE. It consists of a mixture of various building typologies. The buildings are analyzed by focusing on relationships between the heights and their distances to: 1. park 2. water canal 3. road networks.
Park
Water Canal
Roads
Building Heights
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03 VOLUME DIMENSIONING Volumetric dimensions / typologies are defined along the site constraints as input data for the supervised machine learning system in the following order: 1. High-Rise Mixed-Use Skyscrapers, 2. Mid-Rise Commercial Buildings, 3. Low-Rise Residential Buildings. The conditional rule is added to the system :
1. High-Rise Footprint = S Height = L
2. Mid-Rise Footprint = M Height = M
SMALLER FOOTPRINT = TALLER BUILDING HEIGHT 3. Low-Rise Footprint = L Height = S
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04 SUPERVISED LEARNING Machine learning, specifically the supervised learning process of regression was adopted. The volumetric output is an array of urban fabric embodying the logic initially fed as an input into network.
The graphs below show a comparison between predicted and actual results. The similarity between both graphs is an indication that the script worked to a large extent. If the graphs were different, it is an indication of some errors in the logic of the definition.
This dictates the position and height for the work of architect / developer, and allows the city to tune the area into a desired profile. There are numerous possible applications of this approach to aid urban design. Input parameters may vary to change the nature of the output. One possibility is to use this technique to vary the size of courtyard openings depending on identified distance from the public space.
top site view
perspective view
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05 RESULT
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urban fabric
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GROUP MEMBERS Ali Fayyad Andy Shaw Mohammad Al Shukor
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WORKFLOW
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Site Context as Input
Site Data
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Supervised Learning
Result
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01 SITE CONTEXT _ 02 SITE DATA INPUTS Connectivity 1. proximity to weighted networks, 2. water network, 3. road networks, 4. pedestrian networks.
OUTPUTS Current Density 5. Volume (as Built up Area)
Building volumes for specific locations.
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03 SUPERVISED LEARNING
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04 RESULTS
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encoded memory 1.0 _ machine learning Workshop
PARTICIPANTS Lina Ahmad Ali Fayyad Khaleefa al Hemli Monika Kalinowska Omar Khadoorah Marta Krivosheek Ping Ping Lu
Hayder Mahdi Tang Li Qun Andy Shaw Mohammad Al Shukor Abdoullah Tahseen Alberto Tono
TUTORS
CREDITS
Mateusz Zwierzycki member @Designmorphine, research assistant @CITA
Report prepared in collaboration with MATEUSZ
Zayad Motlib member and founder @dNAT founder @amorphoustudio
ZWIERZYCKI, ZAYAD MOTLIB & MARTA KRIVOSHEEK. Edited by Mateusz Zwierzycki, Zayad Motlib & Marta Krivosheek Graphics by Marta Krivosheek
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http://designmorphine.org/ https://www.facebook.com/designmorphine/ https://www.instagram.com/designmorphine/
http://www.d-nat.net/ https://www.facebook.com/dubai.nat https://twitter.com/dubainat https://www.instagram.com/dubainat/