Portfolio 2021 by Aleksander Mastalski by Aleksander Mastalski

ALEKSANDER MASTALSKI

ALEKSANDER

MASTALSKI Young architect with the vision to elevate the concepts of architectural world using the newest acquirements of technical and scientific field.

computational

designer architect engineer Warsaw

Poland

+48-513-744-200 mastalski.aleksander @gmail.com

Digital footprint

Timeline

Education

Autumn 2020 - Autumn 2021

Master in Advanced Computation for Architecture & Design [MACAD] Institute for Advanced Architecture of Catalonia Barcelona

Autumn 2018 - Summer 2019

One year Erasmus+ exchange:

Master in Advanced Architecture Hochshule Anhalt University of Applied Sciences, Dessau Institute of Architecture. Germany

Autumn 2015 - Autumn 2019

Architecture B.Arch. Degree of Architect Engineer Lodz University of Technology, Poland

English Polish French German Russian

fluent native basic basic basic

Professional Experience

Architect assistant WMA ARCHITEKCI Warsaw, Poland, summer 2017 Work with a team of architects in BIM environment, designing mostly multi-family housing developments. The responsibilities included designing apartments, preparing technical documentation, shaping the BIM model and making buildings information survey.

Remote work, march till may 2018 Preparing interior design and complex documentation of apartments in multi-family housing developments based on specific guidelines from the investor.

Architect assistant RM STUDIO Lodz, Poland, 2015-2021 GSPublisherVersion 0.73.100.100

Part time job, that required all phases of architectural design process that ranged from consulting early stages of project with clients, early drafts, multi-field coordination of projects with specialists, preparing complex BIM model, up to finalizing technical documentation and preparing it to be submitted to municipality office to receive building permit. Part of the responsibilities were also preparing BIM environment, making building surveys and assisting during building inspection on the site. Most of the buildings that were designed or renovated were single-family houses, but the work also included bigger developments like the renovation of manor house complex that is listed in Polish national register of historic monuments.

Table of Contents CLICK ON ANY NAME TO NAVIGATE TO PROJECT

1 Modular wood pavilion IAAC 2020

2 Modular Edifice IAAC 2020

3 Athenaeum Lunaris IAAC 2021

4 Urban Voids IAAC 2021

5 Training the Topology IAAC 2021

6 Architectural Intermediates [thesis project]

IAAC 2021

7 Bachelor thesis LODZ 2019

Adaptive city car sponsored by AUDI

Hinterland Studio DIA 2018

DIA 2019

10 Example of professional work

LODZ 2020

parametric design fabrication focus coding

BIM urban scale

machine learning 11

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COMPLEX FORMING: Modular wood pavilion Faculty: Arthur Mamou-Mani Assistant: Krishna Bhat IAAC Winter 2020

http://www.iaacblog.com/programs/modular-wood-pavillion/

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PROJECT INTRO

The objective of the Complex Forming seminar was to take inspiration from nature and recreate it. Furthermore the aim was not only to develop Grasshopper script for abstract shape, but also to create a design which could be fabricated. First step was using Grasshopper to try and mimic the behavior of slime mold. Next was to recreate the actual shape of natural system. [the blogpost include animated showcase of project]

TIMELINE

4. “Slime molds are among the world’s strangest organisms. Long mistaken for fungi, they are now classed as a type of amoeba. As singlecelled organisms, they have neither neurons nor brains. Yet for about a decade, scientists have debated whether slime molds have the capacity to learn about their environments and adjust their behavior accordingly.” @Katia Moskwitch, quantamagazine

Natural system recreation 1. Picking half-sphere for simulating growth pattern of the mold 2. Using voronoi (just for a platform for simulation) 3. Specifying origin location of mold and food 4. Finding shortest way to connect slime with food

Application of the system on different surface 1. Picking Enneper’s surface for presentation purpose, populating with points and creating connection using Incestuous Network 2. Smoothening the lines using woolly paths loop 3. Creating further, smaller subdivisions 4. Adding volume using Cocoon

1. 1.

4. 15

Initial fabrication strategies [the requirement was to use wood]

CNC cut prototype

The initial fabrication strategies were either to use 1D or 2D CNC cut plywood (visualised on the top left figure), or to use reciprocal plank connection. First idea was scrapped due to extensive wastage of material during CNC cutting. Outcomes of this experiment were not satisfying. Therefore, another approach had to be found. Form finding via subdivision of the surface was the next step in project development.

Random reciprocal stacking achieved with WASP plugin

Second approach was further developed as random aggregation achieved by stacking planks (bottom left figure) were too random and not structurally stable.

The form found in early stages was used as the boundaries of the surface which was created for further experiments. Preparation for structural analysis with Karamba

outcomes of the early algorithm

UV subdivision

During initial tessellation attempts Enneper’s surface isolines with center point in the middle caused issues with immensely high number of small faces in the middle. In order to prepare model for Karamba analysis, further dividing methods were used.

selected piece of the surface

voronoi subdivision

Preparation for structural analysis with Karamba

To address specific matrix of Enneper’s surface isocurves and to provide reasonable subdivisions for analytic mesh further study was done. By extracting isocurves and creating increasing number of subdivisions and connections, a simplified mesh was created. The manual approach was taken to better understand the mesh to brep transition and to gain better control over the process.

Triangular + Quad subdivisions

Triangular subdivisions

Karamba analysis

First structural analysis was an exploration of different approaches towards fabrication. The first one was done using shell model, the second using beam. Whole surface was analyzed to better understand how it behaves. Self-weight, gravity and wind were the cases analyzed in simulation. The second step in simulation was to go back to trimmed surface and repeat the analysis.

Preparing the grid for the trimmed mesh

Analysis of full surface

Analysis of trimmed surface

Using Karamba data to enhance structural properties

Further experiments conducted was checking if by increasing the height of the shell, based on the utilization values, the structural stability can be increased. The other objective was to reduce the amount of material used. Found geometry was then meshed to create boundaries and set the model for wasp aggregation. By using octahedrons and tetrahedrons and aggregating them together using WASP plugin, a 3-dimensional grid of the same length was created.

Shell model analysis

Showing the utilization values

Utilization values represented by color gradient

Adding depth based on util. values

Creating close mesh

Performing aggregation using Wasp plugin

Karamba analysis

The geometry achieved during grid experiment was taken back for analysis in Karamba to check whether initial objectives were met. The first analysis was done in secluded environment without the wind-load taken into account. Thanks to this it was possible to drastically decrease the section(9×9 cm to 1×1 cm and 4058 kg to 177 kg) in comparison to earlier analysis. That would highly decrease the cost of the system if used in interiors. The final analysis was done with the wind-load result case to make sure that the structure can be built outside. This search forced the beam section to be increased to 4×4 cm, but the overall mass was still much lower then while using the original grid system (4058 kg compared to 2836 kg)

First analysis

Second analysis

Final fabrication strategies [regular grid]

Final ideas for fabrication was to either use steel plates as way to join pieces or to use the self-supported approach. The element used were 5909 4 x 4 cm wooden planks, 50 cm length for regular grid and 70 cm for reciprocal approach. Joint system for the second method is custom plug and play nestjoint system. This method allows fabrication to be as simple as possible. As the grid consists of the elements of the same length and is based on tetra and octa-hedrons it has a finite number of joint types, which can be prefabricated or 3d-printed.

Final fabrication strategies

Reciprocal grid

Regular grid

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1 Renders

http://www.iaacblog.com/programs/modular-wood-pavillion/

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2 http://www.iaacblog.com/programs/modular-edifice-modular-thinking-way-designing-sustainable-building/

Advanced Computation for Environmental and Structural Design Studio Modular Edifice IAAC Winter 2020

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Advanced Computation for Environmental and Structural Design Studio Modular Edifice IAAC Winter 2020

Studio: Faculty: Rodrigo Aguire Asst: Oana Taut Environmental Analysis: Faculty: Angelos Chronis Asst: Aris Vartholmaios | Sarah Mokhtar Structural Optimization Strategies: Faculty: Manja van de Worp Asst: Hanna Lepperød

Studio syllabus: With the growth of urban areas, cities are becoming focal points for human life, however they still lag behind in sustainability. When designing our built environment, it is therefore important to consider the consequences of human settlements on the health of ecosystems. With the integration of computational technologies into architecture, designers are not only capable of simulating the impact of external forces in the digital environment, but also create design workflows to have constant analysis feedback of the building performance, allowing to optimize the ecological footprint of architectural interventions. From the physical spaces of our built environment to the networked spaces of digital culture, algorithmic and computational strategies are reshaping not only design strategies but the entire perception of Architecture and its boundaries. With this said, this research studio will focus on emergent design strategies based on algorithmic design logics applied in the building environment. To allow a greater understanding of building performance, students will be directed to analyze critically novel existing projects. Through these case studies, students will be able to gather information which will guide them to generate advanced design strategies focusing on environmental and structural optimization. Through constant discussion and feedback students will find new ways to extract and use data as the main design driver to develop an architectural project in the urban context. With the help of real time analysis students will be able to interact closer to the digital model, allowing them to understand better the behavior of the building as the design evolves. The final outcome will be the design of a building structure and facade system that can respond to efficiency and sustainability. The projects will have synergy with the rest of the digital tools seminars, allowing the students to apply the knowledge received into a practical and speculative design proposal. This investigation will propose a unique typology and technological mediation that can have a profound impact not only on local surroundings but can be applied on any context with similar issues.

TEAM: + Aleksander Mastalski + Keshava Narayan + Krishnanunni Vijaykumar Modular entities have objects which repeat with respect to each other. eg. Cells. Modularity can also lead to sustainability as the formwork used to build one module can be repeated to build the other modules hence saving material, money, as well as time. Modularity can allow for phase wise construction which can help in incremental building. Objectives: -To use modular thinking as a way of sustainable building by developing a modular building almost entirely in timber and upcycling existing materials as a carbon reducing and sustainable approach. -To look into how non visual aspects like radiation/sound/noise and wind can also shape the design with the help of digital simulations. -Integrating active environmental systems that contribute to both structural integrity and optimize environmental impacts. -To address the lack of public space and to increase the quality of public/social life in Mumbai.

Links to detailed project description

http://www.iaacblog.com/programs/modular-edifice-modular-thinking-way-designing-sustainable-building/

Studio

http://www.iaacblog.com/programs/modular-edifice-truncated-octahedron/

Environmental Analysis

http://www.iaacblog.com/programs/modular-aggregations-structures-modular-edifice/

Structural analysis

Location: The site is located in a bustling industrial area next to a comparatively idle port in the city of Mumbai. The urban fabric consists of majorly commercial buildings and warehouses as this is an industrial area. The site is closer to the east coast of the Mumbai City.

2 1. SITE ANALYSIS

Plot: 300 x 200 m

Commercial

Industrial

Access to site: The site is accessible through road and rail and within 5 minutes of walk from the nearest railway station (Reay Road) and it is located on two main roads. Hence the site is a potential commercial centre lying undeveloped because of the heavy noise from the surroundings.

Period 1

Period 2 Environmental analysis: Total radiation and wind rose are compared for the two periods 1. October to March 2. April to September The comparison revealed the variations in radiation and wind pattern for the two periods. Period 1: The highest radiation values over the year are from South East to South 120 to 220 Degrees. Constant wind during this period are from North with higher wind speeds from WNW to NNE Period 2: The radiation is relatively uniform with higher radiations from east and west directions. Major wind flow is from west direction from SW to WNW Second analysis shows the radiation and temperature values throughout the year .

Daily temperature and radiation throughout the year:

Noise analysis: The site being located in an industrial plot is subject to noise above safe levels all day rendering it unusable all day. [Red dashed line shows the safe limit] Hence, the building should be placed in a way that the harmful sound does not penetrate to the site.

2. DESIGN STRATEGIES

1. Shield the site from noise by placing the building along the boundary of the site

Objectives: • To use modular thinking as a way of sustainable building by developing a modular building almost entirely in timber and upcycling existing materials as a carbon reducing and sustainable approach. • To look into how non visual aspects like radiation/sound/noise and wind can also shape the design with the help of digital simulations. • Integrating active environmental systems that contribute to both structural integrity and optimize environmental impacts. • To address the lack of public space in Mumbai and to increase the quality of public/social life in Mumbai. The different conclusions arrived at earlier were compiled together to form the design principles. • • • • •

Noise Light Views towards the sea Cross- Ventilation Incremental building

2. Protect the building from strong light from south and west by using a second skin

3. Promote views towards the sea by increasing the height of th building

4. Break down the building to allow for cross ventilation and for public access into the site

The programmatic requirements for a coworking space in general are studied in order to be incorporated in the proposal.

REQUIRE-

MENTS 3. PROGRAM

To achieve modularity - stackable geometries were analyzed and chosen.

The programmatic requirements for a coworking space in general are studied in order to be incorporated in the proposal. Public spaces like the library, cafeteria

DEVELOPED MODULES

Octahedron serves interlocking much better than a tetrahedron as shown earlier.

Public

The modules were redeveloped, initial function scheme was designed.

Private

DEVELOPED MODULES Octahedron

Truncated Octahedron

A small composition of the Interlocking modules showing the different functions .

DEVELOPED MODULES P R O ThePmodules O S wereE D redeveloped according to PROGRAMS the learnings from the last

SMALL

MEDIUM

LARGE DEVELOPED MODULES

slide to develop the final modules for aggregation.

The proposed pro(in metres) grams for the site are a public retail area with landscaped paths at the site level and a commercial office space for the rest of the building.

The shared pods can be attached to the larger modules which can be a cabin for a single person as well as for 2 people as shown.

SMALL RETAIL SHOWROOM PRIVATE OFFICE

LARGE RETAIL SHOWROOM RESTAURANT

OFFICE BLOCK

4. FORM FINDING site analysis

The initial access points on the site boundary is connected with interior points where maximum interaction is desired. This creates a network of paths which is then further developed to form smooth walkways from the streets, through the building into the green zones. The access points on the boundaries are defined based on the street type with higher number of access from the street with the highest pedestrian activity. The vehicular access into the coworking block is provided from the connecting road with low pedestrian and vehicular activity. Access from the main road into the site is limited.

4. FORM FINDING genetic algorithm

FITNESS CRITERIA The footprint identified was taken ahead in Wallacei to optimize the building for environmental parameters. FV1, FV2 - Maximum exposure towards ocean (East) and South FV3 - Minimum exposure towards West (sun radiation and view improvement)

FV4 - Minimum total sun radiation (yearly analysis) FV5 - Aim for volume range of: 30 000 - 31 000 m3

Wallacei

results

were analyzed and best

performing

shape was chosen for further development.

Volume Comparison (in m3) (in m3) Volume Comparison

Radiation Comparison (in kWh/m2) Radiation Comparison (in kWh/m2)

5. INTEGRATION OF PROGRAM AND FORM

Shape developed in earlier steps is now used as a bounding geometry for modular aggregation done using WASP plugin. The facade of the building grows down into the site forming a barrier envelope from sound and high radiation but also provide natural ventilation.

A|0_A|6 A|6_A|0 A|1_A|7 A|7_A|1 A|2_A|4 A|4_A|2 A|3_A|5 A|5_A|3

The envelope meets green zone of the site and merges the building, the public paths and the green zone into A|0_B|6 A|6_B|0 one structure that works together. A|1_B|7 A|7_B|1 A|2_B|4 A|4_B|2 A|3_B|5 A|5_B|3

6. MASTERPLAN STRATEGIES [most optimal module location]

1. Defining area on ground floor for module aggregation based on building geometry and site analysis. 2. Dividing site into equally spaced grid that is same as biggest module span is: [ 8 * sqrt(2)]m. 3. Running simple optimization simulation to figure out best initial aggregation.

The access points on the boundaries are defined based on the street type with higher number of access from the street with the highest pedestrian activity. The vehicular access into the coworking block is provided from the connecting

road

with low pedestrian and vehicular activity. Access from the main road into the site is limited.

2 7. STRUCTURAL ANALYSIS

1. Setting up ground and communication modules

2. Setting up the remaining modules

The final form chosen was structurally analyzed to understand the benefits and shortcomings of the form which can help in programmatic distribution.

4. Adding structural beams

STRUCTURAL ANALYSIS STRUCTURAL ANALYSIS

The structure was analyzed using various models and result cases: Result cases:

1.Result Gravity 1. cases: Gravity 2. 1.WindGravity load analyzed for 2 predominant directions 2. Wind load analyzed for 2 predominant directions a.2.West facade area 4226 m2 a. -West - facade area m2 Wind load analyzed for 24226 predominant directions b. North facade area 3505 m2 a. West facade area 4226 b. North - facade area 3505 m2 Force: kN/m2 area 3505 m2 b. North1.5 - facade Force:Force: 1.5 kN/m2 1.5 kN/m2 load 3. 3.FloorFloor load Total load area: 12 928m2 Force: 1.5 kN/m2 3. Floor Total area: 12 928m2 Force: 1.5Force: kN/m2 Total area: 12 928m2 1.5 kN/m2

3 3

2.a 2.a

2.b 2.b

3. Adding analysis

floors

5. Highlighting structure

for

the

static

whole

SHELL ANALYSIS GRAVITY Shell WIND AND FLOOR LOADS

analysis

Shell height:

6 cm

Mass:

8 107 700 kg

Result case - 1 gravity

Result case - 2 Wind load

Result case - 3 Floor load

Min util: Max util: Mean util:

0% 91.45 % 13.23 %

0% 16.86 % 2.56 %

0% 27.05 % 3.18 %

Max disp. Mean disp.

3.13 cm 0.59 cm

1.02 cm 0.1 cm

0.65 cm 0.08 cm

Force flow lines FORCE

FLOW LINES GRAVITY WIND AND FLOOR LOADS

Tension lines Compression lines Force ﬂow lines

BEAM ANALYSIS GRAVITY WIND AND FLOOR LOADS

Beam section:

25x25 cm

Result case - 1 gravity

Result case - 2 Wind load

Result case - 3 Floor load

Mass:

1 071 300 kg

Min util: Max util: Mean util:

0.99% 102.35 % 16.86 %

0.64% 49.16 % 8.78 %

0% 74.52 % 8.62 %

0% 102.35 % 15.07 %

Max disp. Mean disp.

18.70 cm 2.29 cm

9.29 cm 1.07 cm

12.88 cm 1.41 cm

18.70 cm 2.29 cm

BEAM + FLOOR ANALYSIS GRAVITY WIND AND FLOOR LOADS

Beam util

Beam section:

30x30 cm

Mass(ﬂoors incl.):

2 706 200 kg

Result case - 1 gravity

Result case - 2 Wind load

Result case - 3 Floor load

Min util: Max util: Mean util:

0.8% 166.69 % 23.54 %

0.69% 161.58 % 19.08 %

0% 166.69 % 17.06 %

0% 126.6 % 14.07 %

Max disp. Mean disp.

25.16 cm 3.25 cm

22.16 cm 2.61 cm

25.16 cm 3.25 cm

17.05 cm 2.03 cm

Floor util

BEAMS THAT MUST BE REINFORCED

Beam util

Result case - 1 gravity

Result case - 2 Wind load

Result case - 3 Floor load

0.8% 166.69 % 23.54 %

0.69% 161.58 % 19.08 %

0% 166.69 % 17.06 %

0% 126.6 % 14.07 %

25.16 cm 3.25 cm

22.16 cm 2.61 cm

25.16 cm 3.25 cm

17.05 cm 2.03 cm

Beam section:

30x30 cm

Mass(ﬂoors incl.):

2 706 200 kg

Min util: Max util: Mean util: Max disp. Mean disp.

Floor util

INCREASING STABILITY Introducing steel “core” to increase stability

Beam util

Beam section: Steel “core” sec.: Mass(ﬂoors incl.):

24x24 cm 8x8 cm 2 425 100 kg

Wooden beams

Steel core

Min util: Max util: Mean util:

0.8% 166.69 % 23.54 %

0.69% 161.58 % 19.08 %

1.11 % 104.14 % 16.21 %

Max disp. Mean disp.

6.36 cm 1.05 cm

Floor util

LARGE OFFICES || 30*35*17m

8. INTEGRATION OF THE FUNCTION AND ENVELOPE

The achieved modules were later combined into specific function clusters. The white pieces are independent parts of the building and can be rented for any use. Three clusters have been extracted and showcased individually.

LIBRARY / OPEN DESKS || 27*37*22m

CONFERENCE /MEETING ROOMS 33*23*39m

Floorplan example

44 CORE i7

12m

CORE i7

12m

CORE i7

12m

CORE i7

66m 12m

CORE i7

12m

CORE i7

Section A-A'

Sections

OPEN LOUNGE

LIBRARY

PRIVATE OFFICE 2

6m 3m

PRIVATE OFFICE 1

OPEN DESK AREA

6m 3m

CONFERENCE AREA

S TO PANELS

8. INTEGRATION OF THE FUNCTION AND ENVELOPE

Active shading system reacts to the daily sun path to shade the interior of the building from intense radiation. Each panel moves independently based on sensors. This allows to maintain bigger openings without additional cooling. The panel movement can also be used to promote natural ventilation during night. 58% of most radiation heavy parts of the building were chosen as a nest for the additional, external skin of the facade.

CATIONS TO ACE THE PANELS

most radiation parts of the building.

oposed facade s will be placed in hlighted regions.

FINAL MODULE

L MODULE

DVANCED COMPUTATION FOR ARCHITECTURE & DESIGN 2020/2021

Aleksander Mastalski Keshava Narayan

Environmental analysis 27.10.2020

Krishnanunni Vijayakumar

MASTER IN ADVANCED COMPUTATION FOR ARCHITECTURE & DESIGN 2020/2021

Aleksander Mastalski Keshava Narayan Krishnanunni Vijayakumar

Seminar S.1 - Environmental analysis SESSION 3 - 27.10.2020

West

GROUP 1

high

Panels opened fully, total radiation: 682 kWh/m2

Panels half-opened, total radiation: 400 kWh/m2

Panels fully closed, total radiation: 162 kWh/m2

low

As the thermal comfort of both outdoors and indoor spaces was taken into account the decisions which were made earlier were double checked using outdoor thermal comfort analysis. Further analysis are shwocased in furhter pages,.

ILLUMINANCE ANALYSIS FOR FULLY GLAZED OPENINGS - For Summer Month Noon period

MASTER IN ADVANCED COMPUTATION FOR ARCHITECTURE & DESIGN 2020/2021 Seminar S.1 - Environmental analysis SESSION 3 - 27.10.2020

ANNUAL SUNLIGHT EXPOSURE

Aleksander Mastalski Keshava Narayan Krishnanunni Vijayakumar

GROUP 1

FOR FULLY GLAZED OPENINGS

Spatial daylight autonomy : 100% of the floor space received more than the required level of 300 lux value for daylight illuminance Annual Sunlight exposure:: About 78.43% of the floor space received more than 1000lux of direct sunlight for more than 250 occupied hours.annually. MASTER IN ADVANCED COMPUTATION FOR ARCHITECTURE & DESIGN 2020/2021 Seminar S.1 - Environmental analysis SESSION 3 - 27.10.2020

Aleksander Mastalski Keshava Narayan Krishnanunni Vijayakumar

GROUP 1

ILLUMINANCE ANALYSIS

TRANSPARENT

FOR PATTERN 1 - For Summer Month Noon period

ANNUAL SUNLIGHT EXPOSURE

OPAQUE

TRANSPARENT

FOR PATTERN 1 - For Summer Month Noon period OPAQUE

Spatial daylight autonomy : About 80% of the floor space received more than the required level of 300 lux value for daylight illuminance Annual Sunlight exposure:: About 40% of the floor space received more than 1000lux of direct sunlight for more than 250 occupied hours.annually.

ILLUMINANCE ANALYSIS

TRANSPARENT

FOR PATTERN 2 - For Summer Month Noon period

OPAQUE

ANNUAL SUNLIGHT EXPOSURE FOR PATTERN 1 - For Summer Month Noon period

Spatial daylight autonomy : About 76.8% of the floor space received more than the required level of 300 lux value for daylight illuminance Annual Sunlight exposure:: About 32.56% of the floor space received more than 1000lux of direct sunlight for more than 250 occupied hours.annually.

ILLUMINANCE ANALYSIS FOR PATTERN 2 WITH ATRIUM WALLS FULLY GLAZED- For Summer Month Noon period

ANNUAL SUNLIGHT EXPOSURE

TRANSPARENT

OPAQUE

TRANSPARENT

FOR PATTERN 2 WITH ATRIUM WALLS FULLY GLAZED- For Summer Month Noon period OPAQUE

Spatial daylight autonomy : About 90.2% of the floor space received more than the required level of 300 lux value for daylight illuminance Annual Sunlight exposure:: About 38.4% of the floor space received more than 1000lux of direct sunlight for more than 250 occupied hours.annually.

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2 Renders

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Building Information Modelling and Smart Construction Studio ATHENAEUM LUNARIS IAAC Winter 2021

http://www.iaacblog.com/programs/athenaeum-lunaris-hospital-research-center-moon/

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Building Information Modelling and Smart Construction Studio ATHENAEUM LUNARIS IAAC Winter 2021

http://www.iaacblog.com/programs/athenaeum-lunaris-hospital-research-center-moon/

Studio syllabus: Using an experimental research-based approach, the goal of the studio is to explore key concepts of the Building Information Modelling paradigm such as Parametric modelling, Documentation, Interoperability and inter-collaboration through a practical approach. During the studio students will develop a complex-formed architectural project with a certain level of detail in an entirely fictional context and in a collaborative scenario, and will be required to produce project documentation while undergoing changes during different stages of the development. All the projects will be tied by a common infrastructure whose changes will influence each of the projects at their cores, which will force the students to create interfaces that can parametrically adapt during the project development. The second semester of the MACAD revolved around BIM and collaboration. Studio class guided by David Leon, he felt it would be appropriate for teams to work together on a moon settlement. Each team had to chose a function, team 1 was responsible for making a hospital and a research center. It is needless to explain the necessity of a hospital, but a research center is something very necessary for humans curiosity and prosperity. A research center went in line with the collaboration idea, as it would be a collaboration hotspot between all the other groups.

Faculty: David Andres Leon Asst: Oana Taut

+ Yara Aseeri + Andrei Okolokoulak + Aleksander Mastalski

ATHENAEUM

LUNARIS Medical center is among the MOST vital necessities for any colony.

Curiosity got us to the Moon. And once there - it will propel humanity forward.

Athenaeum or ath-e-ne-um

[ ath-uh-nee-uhm, -ney- ]

noun <HOSPITAL> <ER + SURGERY ROOM> <PHARMACY> <MEDICAL ENGINEERING LAB> <PHYSIO + PSYCHIC THERAPY>

An institution for the promotion of literary or scientific learning.

MOON COLONY LAYOUT

HOSPITAL

RESEARCH

PROGRAM

CENTER PROGRAM

name

number of number total of area total area name rooms area[m2] rooms [m2] area[m2] [m2]

Function Reception/waiting Reception/waiting room 1room and space relations, room room 1 SPACE DecontaminationDecontamination SYNTAX Lavatory Lavatory 1 Internist room Internist room

The first step Surgery Surgery 1 before creating space ERsyn/ Resuscitation ER room / Resuscitation1room tax was deciding Warehouse/ surgery Warehouse/ surgery 1 robotic robotic on function and Recovery room Recovery room 1 relation in bePatient rooms observation Patient rooms observation 1 (2ppl) (2ppl) tween. Patient rooms critical Patientcare rooms critical care 1 (2ppl) (2ppl)

20 1

20 20

10 1

10 10

12 1

12 12

16 2

32 16

24 1

24 24

18 1

18 18

12 1

12 12

16 1

16 16

30 1

30 30

30 1

Dirty changing room Dirty changing room 1

36 1

36 36

Staff lounge+lavatory Staff lounge+lavatory 1

24 1

24 24

Medical Laboratory Medical Laboratory 1

15 1

15 15 15

Bio-Tech Lab

number of name rooms BioGrowth 1 Lab [Stem Cell]

Robot Robot 4 Bays Prototype Area Prototype Area Bays Fabrication / Fabrication / 1 Workshop Workshop

1 15

15 15

4 15

15 60

150 1

150150

150

210

Loading Area

2 10

10 20

RF Signal

Communi cation RF Signal Antennae

Communi cation 2 Antennae

5 10

Data Center / E-library Office Zone Meeting Room

Data Center / 1 E-library

1 30

30 30

Meeting 1 Room

1 36

36 36

Offices

Offices 3

3 10

10 30

Office Zone

Lavatory Utility

Lavatory 3

3 12

12 36

Airlocks & suits

Airlocks & 3 suits

5 15

Rover airlock

Rover 1 airlock

1 20

20 20

1 20

20 20

Utility

5 3

Rover airlock

20 1

20 20

Water & oxygen

Water & 1 oxygen

20 1

20 20

Communi cation

334 TOTAL

334

TOTAL

area[m2] total area [m2] total area [m2]

Docking Platform/ 2 Ring

Rover airlock

number of area[m2] rooms

Docking Platform/Area Loading Ring

Airlocks & suits Airlocks & suits

Water & oxygenWater aggregate & oxygen aggregate 1

30 30

name

BioGrowth Bio-Tech Lab Lab [Stem Cell]

TOTAL

TOTAL 748

748

Pharamcy

W.C 0

Lift 0

Airlocks 0

Reception

Vital Sign

Program was a very important aspect for our project. Hospitals and Education centers rely on good connection logic between its spaces, so it was essential for us to understand the space syntax and make these tables that organize and shape our design. LNS Pub

Hospital

area 20

LNS Pub

Admi Phar W.C macy n 0

Lift 0

Admin

Pharamcy

W.C 0

Lift 0

Airlocks 0

Reception

Vital Sign Clinics

Surgery

W.C 01

Lift 01

Staff

Med Lab

W.C 02

Lift 02

Nurse Sta

Recovery

ICU

W.C 03

Lift 03

Staff

Med Lab

W.C 02

Lift 02

Nurs Reco e very Stat

ICU

W. C03

Lift03

x7 x8

x10

x13

15 20

connection logic for gh

5 12

Lift 02

Nurse Sta

Recovery

ICU

W.C 03

Lift 03

Research area

Admin

Staff

x20

Robo W.C ts 02 Bay

x22

RF Data W.C Lift Signa Airlocks 02 Cent 03 02 l

x22

x15

x16

x17

x16

x17

x19

x20

Loadi Rove ng rs

O2/H Airloc W.C 20 01 ks 0 Sup

Lift 0

LNS Res

BioTe Lift Admi Proto Airlocks 01 Staff ch 01 n

Robo W.C ts 02 Bay

x18

19 20 22

x19

x20

x21

x20

x22

RF Data W.C Lift Signa Airlocks 02 Cent 03 02 l

Lift3

17 21

14 22

x18

x22

17 20

x19

x21

x22

x15

x16

x17

x22

connection logic for gh

x1 x1

127

2345

x14

13 16

x12

13 16

x19

13 15

x11

x13

x19

x13

14 22

x13

W.C 01

19 20 22

x13

x11

x12

x13

9 13 12

x17

x18

x13

x10

Airlocks 0

x18

x10

x17

9 10 13

x17

17 21

0 13

Rovers

Proto

x16

0 9 13 12

150

x16

O2/H20 Sup

0 13

17 20

x15

5 5 12

x14

x11

LNS Res

13 15

x15

x12

x13

x14

x10

x12

x15

Lift 0

x12

x10

x11

W.C 02

Lift 01

x15

Med Lab

Bio-Tech

x22

Airlocks 01

x21

Staff

x20

x19

Lift 01

W.C 01

x17

Med Robots 11

x16

9 10 13

x13

BioTe Lift Admi Proto Airlocks 01 Staff ch 01 n

Clinics

Surgery

x14

O2/H Airloc W.C 20 01 ks 0 Sup

3 4

Figure below shows: • Program function matrix floor by floor connection logic • Common services between floors • Common spaces between hospital and research center • Total area

x15

Loadi Rove ng rs

x12 x13

x10 x11

x12 x13

x7 x8

x9 x10

x12

50 60 15 20

x5 x6

x10

x9 x10

x10

Lift 02

Airlocks 02 16

Data Cent

RF Signal

x20

x21

W.C 03

Lift 03

x10

7 14

9 15

x10 x12

x11

x12

x13

x14 x15

x15

x14

x15

x16

x20

12 13 15

15 19

11 20

x17

18 19

x18

x19

x17

x19

12 x13

x14 x15

x11

Robots Bay 13 W.C 02

x20

x17

x20

connection logic for gh

Lift3

Inputting data into self-made grasshopper algorithm: Loading

Rovers

O2/H20 Sup

Airlocks 0

W.C 01

Lift 01

Airlocks 01

127

2345

x5 x6

x10

Bio-Tech

150

Proto

Admin

Staff

x11

Lift 01

LNS Res

x10

Lift 0

LNS Lift 0 Res

Research

Med Robots 11

area

Airloc Rece Vital Clinic Surg Med W.C ption Sign s ery Robo 01 ks ts

Lift 02

Airlocks 02 16

Data Cent

RF Signal

W.C 03

Lift 03

x8 x9 x10

x10

x10 x12

x11

x12

x13

x14

7 14

9 15

x15

x14

x15

x17

x20

x18 x19

x19 x20

12 13

x20

15 19 11 20

x15

15 16

x17

18 19

x18

x19

x16 x17

12 x13

x14 x15

x9 x10

x11

Robots Bay 13 W.C 02

x11

SPACE SYNTAX

METHODOLOGY behind self made script 1

Input connections, floor distribution and room areas, divide big areas into small

Create connection lines with specified lengths for Kangaroo to aim for.

Run kangaroo solver, organize outputs

Create rooms from spheres

Create clean corridor connections

Generate plans

floor

Simplified web version of the algorithm was deployed on HEROKU using rhino compute server: https://appserverclass.herokuapp.com/examples/server_space_syntax_test/

Fully automated process of creation of the floorplans from spheres [based on ground floor of the building]

BUILDING STRUCTURAL CORE - TOPOS plugin

METHODOLOGY

Due to complicated shape achieved by using the space syntax algorithm topological optimization was used to ensure structural integrity of the building. The GPU based TOPOS plugin for Grasshopper was used for the analysis.

1 Creating optimization boundaries with loads 2 Highlighting loads and supports 3 Extracting analytical mesh 4 Refining mesh

final

EXTERNAL SKIN KANAGAROO inflation Using the massing from space syntax, a shell is generated through Kangaroo inflation. Interoopperability

grasshopper

rhino

revit

[Space Syntax input]

[Internalized data ]

// Connection logic // Circulation design // Diagrammatic data

// Add level // Floors // Walls // Structural Frame // Curtain Panel

// Topography // Floor plans // Sections // detailed section // Exploded diagrams // Renders // Schedule // Sheets

[Structural Envelope Elements] // Inflation envelope morphology // Structural beam // Regolith glass panels

2 3 A105 A108

H_Admin H_W.C

H20_O2 Supply

H_LNS Public

LNSResource

L_Lift

TECHNICAL DRAWINGS Rhino inside Revit

Pharmacy

H_Airlock

H_Left 1 A105

L_Airlocks

Rovers ER

Room Legend ER

H20_O2 Supply

2 3 A105 A108

H_Admin H_Airlock H_Left 1

H_LNS Public

A107

H_W.C Reception

L_Airlocks

Surgical Robot

L_Lift LNSResource L_Prototype

L_Lift

L_W.C

H_W.C

H_Lift

Surgery 1 A105

Pharmacy Rovers

Bio Tech

LEVEL 00 1 : 100

L_Airlock

Vital Sign

Clinic

2 3 A105 A108

L_Airlock

Robots Bay

Medical Lab L_Admin

Hospital Staff

L_Lift H_Lift 1 A105

H_W.C L_Staff

L_W.C

Room Legend

2 A105

H_Lift H_W.C Hospital Staff L_Admin L_Airlock

L_Lift

A107

Recovery

L_Staff

ICU

L_W.C

L_Data Center

Medical Lab

LEVEL 02 1 : 100

Robots Bay L_Lift

H_Lift H_W.C

Nurse Station 1 A105

L_W.C

L_RF Signal

Room Legend H_Lift H_W.C ICU

L_Data Center L_Lift

2 A105

LEVEL 03 14.00

TECHNICAL DRAWINGS Rhino inside Revit

LEVEL 02 10.00

LEVEL 01 6.00

LEVEL 00 2.00

Section 1 1 : 100 1 A105

LEVEL 03 14.00

LEVEL 02 10.00

3 A108

LEVEL 01 6.00

LEVEL 00 2.00

Section 2 1 : 100

2 3 A105 A108 2 3 A105 A108

LEVEL 03 14.00 LEVEL 03 14.00

LEVEL 02 10.00 LEVEL 02 10.00

LEVEL 01 6.00 LEVEL 01 6.00

LEVEL 00 2.00 LEVEL 00 2.00

1 A105 1 A105

LEVEL 03 14.00 LEVEL 03 14.00

LEVEL 02 10.00 LEVEL 02 10.00

LEVEL 01 6.00 LEVEL 01 6.00

LEVEL 00 2.00 LEVEL 00 2.00

TECHNICAL DRAWINGS Rhino inside Revit

MASTER IN FOR ARCH

// 02 - BIM

// GROUP NU

Yara Gadah

// PROJECT

Building Components

3D Print Regolith Skelton

Regolith Glass Panels

Structural Frames

Droped Ceiling

THE MOON + S

LOCATION: 89.9

Interior Walls

Exterior Walls

REV

PROJECT

Hospit CLIENT

BIM SHEET NAME

Slabs

Level 00

E 3D_

DRAWING NUM

Level 01

A109

Level 02

SCALE (@ A1)

Level 03 DRAWN BY

Author

Building strategies

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me nu

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Artificial Intelligence in Architecture Studio URBAN VOIDS IAAC Spring 2021

Faculty: Angelos Chronis Asst: Lea Khairallah https://city-lab.wixsite.com/urbanvoids

+ Aleksander Mastalski + Amal Algamdey + Amar Gurung + Felipe Romer + German Bodenbender Big urban data now being easily available online, there is an opportunity to uti-

Studio syllabus: That artificial intelligence will fundamentally change our lives is not any more a matter of debate. It’s a reality. What is also a reality is that the very same concepts that led to the coinage of the term have been around for more than a century. Still, it is the combination of vast computational power and data that has led to the explosive development of these concepts today. Although that explosive development is obvious in big tech and its applications in our daily lives, it is not that obvious in all other fields. For many, the architecture, engineering and construction (AEC) industry is at the right moment for an equally explosive development that may transcend all aspects of our urban environment. The constantly growing development of AI-related generative design methods, construction processes and data driven analyses are resembling the fertile ground of the early computational design days. However, this promising development of novel digital tectonics also brings with it similar pitfalls to that early computational design era. The imposition of an overly complex methodology of design that fails to address fundamental real challenges of our urban condition today. Climate change, pandemics, growing inequalities are just a few of the challenges that would greatly benefit from AI-driven design processes right now and as researchers, designers and evangelists of an augmented intelligence architecture, we are here to discover the ways. In the “Artificial Intelligence in Architecture” studio we aim to explore the ways that the state of the art AI advancements can help us design and construct more efficient, sustainable and livable urban environments.

lise this information to generate new relationships between various features within the urban fabric. This new information will be useful not only to architects and developers but also to any individual or institutions looking to understand the urban features whether it is to set up a new cafe, particular shop, housing, school or a clinic. Urban Voids is a data-driven approach to analyse and predict potential locations for the addition/intervention of amenities within the city. The predictions and scores are based on a series of urban analyses, simulations and the use of KMeans clustering. The aim is to create a tool that will work on a feedback loop system where the information is constantly being updated. At the back end, there are the various analysis, simulations and clusterings, the results from this are then being visualised in a web-based platform (Mapbox) and to complete the loop, the user inputs a new location and amenity type to generate a new prediction and scoring for the new information

+ 01+

PROJECT CONCEPT

OUR GUIDING PRINCIPLES

04+

DATASET CREATION

EXTRACTING / COMPILING / CLUSTERING

02+

PROJECT REFERENCES

03+

PROJECT METHODOLOGY

STATE OF THE ART

DECODING THE PROCESS

05 +

MACHINE LEARNING

06 +

WEB APPLICATION

K-MEANS CLUSTERING

URBAN VOIDS

+PROJECT FRAMEWORK

1. PROJECT CONCEPT OUR GUIDING PRINCIPLES

CITY

METRICS

BLOCK ANALYSIS

By 2050, 68% of the world population will be

MACHINE LEARNING

living in cities, and in order to best accommodate this rapid urbanization growth while making cities more sustainable, livable, and

FEATURE ENCODING

equitable,designers must utilize qualitative and quantitative tools to make better in-

WEB BASED

URBAN DESIGN

formed decisions about our future cities. Our project goal is to develop a web-based

process that allows users, designers and THE PROJECT IS CONCEIVED AS A DATA DRIVEN governments to better understand the morphology of the city by creating a scoring re-

APPROACH TO ANALYSE AND UNVEIL THE HIDDEN lationship between its nodes. OPPORTUNITIES URBAN FABRIC THE PROJECT IS CONCEIVED AS AON DATATHE DRIVEN APPROACH TO ANALYSE AND UNVEIL THE HIDDEN OPPORTUNITIES ON THE URBAN FABRIC

CAPTURE ALL BUILDINGS

CALCULATE DISTANCES & WALKABILITY

CLUSTER BASED ON PERFORMANCE

COMPARE MODELS & AREAS

PLACE & ANALYZE NEW AMENITIES

REFERENCES 2.PROJECT PROJECT REFERENCES

MORELA

THE STATE OF THE ART

PROJECT REFERENCES

KPFUI

WEB-BASED TOOL THAT ALLOW THE FINAL USER, THE RESIDENTS, TO PROVIDE FEEDBACK ON TOOL THE FUTURE GUIDELINES WEB-BASED THAT ALLOW THE FINAL FOR USER,THE THE CITY RESIDENTS, TO PROVIDE FEEDBACK ON THE FUTURE OF LOS ANGELES GUIDELINES FOR THE CITY OF LOS ANGELES https://more-la.superspace.agency/ https://more-la.superspace.agency/

INTERACTIVE WEB-BASED URBAN MAP THAT DISPLAYS THE BUILDING PROPERTIES IN CONJUNCTION WITH THE PEDESTRIAN MOVEMENTS AT DIFFERENT TIMES OF THE WEEK https://opening-hours.kpfui.dev/

3. PROJECT METHODOLOGY DECODING THE PROCESS

The project is conceived as a data-driven approach to analyze and unveil the hidden opportunities on the urban fabric by capturing all buildings, calculating distances and walkability, clustering based on performance, comparing models/areas, placing and analyzing new amenities. The first step is collecting data from open street maps OSM into a CSV file to calculate walkability scores using python. The second step is to cluster all different scores gathered from python and networkX toPROJECT be later visualized within a web interface. The final step is the user input, whether it’s a location METHODOLOGY or amenity type that will feed into the database that will recalculate the scores resulting in a new score and clustering based on the new input.

PYTHON NETWORKX

MACHINE LEARNING

MAPBOX / CARTO

INTERVENTION

COLLECT & CLEAN DATABASE (GOOGLE SHEETS)

AMENITIES WALKABILITY SCORES

K-MEANS CLUSTERING

ANALYSE RESULTS IN A WEB INTERFACE (CARTO)

INPUT/PROPOSE NEW LOCATION AND TYPE OF AMENITY

RECALCULATE SCORE

OSM DATABASE

UPDATE CSV

GOOGLE SHEETS

4. DATASET CREATION EXTRACTING/ COMPILING/ CLUSTERING For the reliability and efficiency of the process, python and OSMX libraries are used to extract the data from open street maps to provide the script with the city-CRS, which extracts three main data frames, points of interest, pedestrian network, and address points. After that, cleaning and adjusting the data that is used for K-Means clustering

+PYTHON

NEW TAG SYSTEM

NETWORKX GEOPANDAS POINT OF INTERESTS

higher_education

green_area

primary_education

bbq_area

spec_healthcare

base_shopping

hospital

sport_facility

pharmacy

tourism

food

bus_stops

entertainment

railway_stations

nightlife

place_of_worship

PROVIDE SCRIPT WITH: -CITY -CRS

PREPROCESSED GRAPH (PEDESTRIAN NETWORK)

PEDESTRIAN NETWORK

DATA CLEANING & ADJUSTING

(LIBRARY BASED ON NETWORKX)

DATA READY FOR K-MEANS TAGGED POIs

CLEANED ADDRESSES

OSM DATABASE

BUILD PANDANA NETWORK

PREPARED DEFINITION PROCESSING DATA

ADDRESS POINTS

Calculates shortest walk distances from every point to n number of closest POIs speciﬁed by tag.

Evaluate scoring based on avg. pedestrian walking time of 1.2m/s and criteria.

Optimized to work fast (calculation for a tag: ‘bus stop’, for the address data frame of 320 000 rows takes less than 3s).

Outputs any or all of distances/walking times/individual scores/avg. scores for speciﬁed tags & adds values straight into geopandas dataframe.

4. DATASET CREATION EXTRACTING/ COMPILING/ CLUSTERING

BUILDING SCORING AND WALKABILITY ANALYSIS BUILDING SCORING // WALKABILITY ANALYSIS 100

WALK TIME

SCORES

100

Destination spot

Highly walkable

Pedestrian friendly

Very Comfortable

+ANALYSIS LIMITATION 60

Comfortable

Somewhat Comfortable

Somewhat walkable

Difficult

URBANO

+ANALYSIS LIMITATION +

20,000 POINTS Very difficult

Extremely difficult

Too far to walk

Bus stops [2]

hospital [2]

Railway stations [2]

Specialized

healthcare [8]

Primary education [4]

higher education [4]

PHARMACY [2]

food [8]

+ ENTER TAINMENT [8]

100 AMENITIES

SPORT FACILITY [8]

nightlife [8]

2,000,000

BBQ AREA [4]

4 HOURS

CALCULATIONS Green area [2]

tourism [8]

Place of worship [4]

+ +

GRASSHOPPER URBANO

BUILDING TAGS BUILDING TAGS // NUMBER CLOSESTPOIs POIs NUMBER OF OF CLOSEST

DESCRIPTION

GRASSHOPPER

The analysis score criteria are shown below, what is more, on the graphs at the bottom of the page we show how much faster our Python calculations run in comparison to ready-made Urbano plugin in grasshopper.

20,000 POINTS

100 AMENITIES

117,000 POINTS

2000 AMENITIES

2,000,000

4 HOURS

243,000,000

25 SECONDS

CALCULATIONS

PYTHON GEOPANDAS

CALCULATIONS

NETWORK X

PYTHON

5.MACHINE LEARNING K-MEANS CLUSTERING

We use a pair plot to understand the pairwise bivariate distribution in our dataset to understand the data and their relationships. The relationship between the combination of variables in our data frame is straightforward. In addition, the Pearson correlation coefficient is used to understand which attributes are linearly related to the predicted set. Also, a biplot is used to overlay both a score plot and a loading plot onto a single graph to visualize high dimensional data onto a two-dimensional graph. Using the elbow method to understand the ideal number of clusters that should be used for the clustering based on the shape and features of the data. The final clustering is plotted for each category onto a two-dimensional plot based on overall performance.

SUPERVISED LEARNING

CLASSIFICATION

MODEL

SHALLOW LEARNING

REGRESSION MODEL

DEEP LEARNING

K-MEANS CLUSTERING MODEL

MACHINE LEARNING UNSUPERVISED LEARNING

CLUSTERING MODEL

K-MEANS

CLUSTERING

SYLLABUS BIVARIATE DISTRIBUTIONS

PAIRPLOT

WE USE A PAIRPLOT TO UNDERSTAND THE PAIRWISE BIVARIATE DISTRIBUTION IN OUR DATASET, WHERE WE CAN SEE THE RELATIONSHIP FOR THE COMBINATION OF VARIABLES IN OUR DATAFRAME

CORRELATION COEFFICIENT

HEATMAP WE USE THE PEARSON CORRELATION COEFFICIENT TO UNDERSTAND WHICH ATTRIBUTES ARE LINEARLY RELATED TO THE SET WE WANT TO PREDICT

PRINCIPAL COMPONENTS

BIPLOT

WE USE A BIPLOT TO OVERLAY BOTH A SCORE PLOT AND A LOADING PLOT ONTO A SINGLE GRAPH IN ORDER TO VISUALIZE A HIGH DIMENSIONAL DATA ONTO A TWO-DIMENSIONAL GRAPH

ELBOW METHOD

LINE PLOT WE USE THE ELBOW METHOD TO UNDERSTAND THE IDEAL NUMBER OF CLUSTERS THAT WE SHOULD USE FOR THE CLUSTERING BASED ON THE SHAPE AND FEATURES OF OUR DATA

CLUSTERED DATA

PLOT

WE PLOTTED THE FINAL CLUSTERED FOR EACH CATEGORY ONTO A TWO DIMENSIONAL PLOT BASED ON OVERALL PERFORMANCES

5.MACHINE LEARNING SCATTERPLOT

On the scatterplot we can see the overall distribution of all the urban data points gatherer for the city of Melbourne. These are 117,359 points in total

117,359 URBAN POINTS 24 DATA FEATURES 87 AMENITIES TYPES

+ MELBOURNE URBAN POINTS

LATITUDE

RBAN OF TS IN

LONGITUDE

PAIRPLOT

We use a pairplot to understand the pairwise bivariate distribution in our dataset, where we can see the relationship for the combination of variables in our dataframe.

OSMID

NAME

AMENITY

LAT

LONG NODE

BUS STOPS

RAILWAY

PRIMARY

HIGHER HOSPITALS

SPEC HEALTH

We use the pearson correlation coefficient to understand which attributes are linearly related to the set we want to predict.

5.MACHINE LEARNING CORRELATION

HEATMAP

OSMID NAME AMENITY LAT LONG

T TO

NODE

Y WANT

BUS STOPS RAILWAY PRIMARY HIGHER HOSPITALS SPEC HEALTH PARHM FOOD NIGHTLIFE ENTERT SPORTS PARKS PLAY REL

CULTURE

REL

PLAY

PARKS

SPORTS

ENTERT

NIGHTLIFE

FOOD

PARHM

SPEC

HOSPITALS

HIGHER

PRIMARY

RAILWAY

BUS

NODE

LONG

LAT

AMENITY

NAME

OSMID

CULTURE

+ + + + + + + +

PRINCIPAL COMPONENT BIPLOT

BIPLOT

WE USE A BIPLOT TO OVERLAY BOTH A SCORE PLOT AND A LOADING PLOT ONTO A SINGLE GRAPH IN ORDER TO VISUALIZE A HIGH-DIMENSIONAL DATA ONTO A TWO-DIMENSIONAL GRAPH

MIN DEPTH MAX DEPTH MIN HEIGHT MAX HEIGHT GREEN AREA VOLUME BUILDING AREA AVERAGE FAR

PC2

We use a biplot to overlay both a FROM THIS, WE UNDERSTOOD THE score plot and a PC’S THAT ACCOUNTS FOR MOST OF loading plot onto THE VARIANCE ON OUR DATA AND a single graph in USE THIS TO SELECT OUR FINAL order toOURvisualize FEATURES TO TRAIN MODEL a high-dimensional data onto a two-dimensional graph.

From this, we understood the pc’s that accounts for most of the variance on our data 05 and use this to select our final feaK-MEANS tures to CLUSTERING train our model. ELBOTH

K-MEANS

WE USE THE ELBOW METHOD TO UNDERSTAND THE IDEAL NUMBER OF CLUSTERS THAT WE SHOULD USE FOR THE CLUSTERING BASED ON THE SHAPE AND FEATURES OF OUR DATA

CLUSTERING

ELBOTH METHOD

OPTIMAL NUMBER OF CLUSTERS

PC1

We use the elbow method to understand the ideal number of clusters that we should use for the clustering based on the shape and features of our data

MEAN SQUARED ERROR

METHOD

10 11 12 13 14 15 16 17 18 19

NUMBER OF CLUSTERS

CLUSTERED

DATA PLOTS

14 05

FEATURES CLUSTERS

HEALTHCARE

EDUCATION

FOOD & ENTRET

RECREATION & SPORTS

SCORE 0 TO 90

AMOUNT

TRANSPORT

5.MACHINE LEARNING

We plotted the final clustered for each category onto a two dimensional plot based on overall performances.

D ON

SCORE 0 TO 90

AMOUNT

S S

SCORE 0 TO 90

BIVARIATE DISTRIBUTIONS

PAIRPLOT

We use a pairplot to understand the pairwise bivariate distribution in our dataset, where we can see the relationship for the combination of variables in our dataframe

ONS

BIVARIATE DISTRIBUTIONS

LATITUDE

LONGITUDE MELBOURNE HEALTHCARE CLUSTER

ONS

LATITUDE

WE USE A PAIRPLOT TO UNDERSTAND THE PAIRWISE BIVARIATE DISTRIBUTION IN OUR DATASET, WHERE WE CAN SEE THE RELATIONSHIP FOR THE COMBINATION OF VARIABLES IN OUR DATAFRAME

TAFRAME

BIVARIATE DISTRIBUTIONS

LONGITUDE MELBOURNE FOOD & ENTERTAINMENT CLUSTER

PAIRPLOT

WE USE A PAIRPLOT TO UNDERSTAND THE PAIRWISE BIVARIATE DISTRIBUTION IN OUR DATASET, WHERE WE CAN SEE THE RELATIONSHIP FOR THE COMBINATION OF VARIABLES IN OUR DATAFRAME

LATITUDE

TAFRAME

LONGITUDE

LATITUDE

O WISE ION IN WE CAN P FOR

MELBOURNE EDUCATION CLUSTER

PAIRPLOT

O WISE ION IN WE CAN P FOR

MELBOURNE TRANSPORTATION CLUSTER

LONGITUDE

6.WEB APPLICATION & DATA VISUALIZATION

Carto is used to visualizing and filtering the information based on the project criteria. Melbourne city is used as a case study to test the different clusters and their relationships and create a web-based application that allows us to analyze and unveil the hidden opportunities for multiple cities. Five cities with different urban conditions were used as case studies for the analysis and comparison: Melbourne, Sydney, Berlin, Warsaw, and Sao Paulo.

URBAN ANALYSIS POPULATION:5 million TOTAL LOTS:117,361

SCORING (50-100PT/25MIN WALK): OPEN SPACE:6,464 LOTS RAILWAY STATIONS: 32,912 LOTS BUS STOP:107,439 LOTS PLACE OF WORSHIP:`53,145 LOTS PRIMARY EDUCATION:49,016 LOTS SPORTS FACILITIES: 11,176 LOTS DATA VISUALISATION & ANALYSIS TOURIST ATT:34,460 LOTS

DATA VISUALISATION & ANALYSIS HEALTH CLUSTER EDUCATION CLUSTER

RECREATION CLUSTER

TRANSPORT CLUSTER

3 - 5 CLUSTER TOTAL:

1 - 5 CLUSTER TOTAL:

3 - 5 CLUSTER TOTAL:

55,868 86

3 - 5 CLUSTER TOTAL:

20,671

27,366

117,361

3,803

DIFFERENT

CITIES ANALYSIS

MELBOURNE, AUSTRALIA

Melbourne

SYDNEY, AUSTRALIA

Sydney

BERLIN, GERMANY

Berlin

WARSAW, POLAND

SÃO PAULO, BRASIL

Warsaw

Sao Paulo

+ DATA COMPARISON

URBAN CONDITIONS URBAN MORPHOLOGY: Waterfront Cities POPULATION:5 million POVERTY INDICES: 15% pop.

MELBOURNE, AUSTRALIA

Melbourne

SYDNEY, AUSTRALIA

Sydney

URBAN CONDITIONS URBAN MORPHOLOGY: Radial Cities POPULATION:1.5 - 3.5 million POVERTY INDICES: 5% to 12% pop.

BERLIN, GERMANY

Berlin

URBAN CONDITIONS URBAN MORPHOLOGY: Dense Cities POPULATION:13 million POVERTY INDICES: 35% pop.

WARSAW, POLAND

Warsaw

SÃO PAULO, BRASIL

Sao_Paulo.

URBAN CONDITIONS POPULATION:5 million TOTAL LOTS:317,865 L.BEST PERFORMANCE: 4,445

5PT (K-MEANS)

1.4%

URBAN CONDITIONS POPULATION:5 million TOTAL LOTS:155,585 L.BEST PERFORMANCE: 2,418

5PT (K-MEANS)

1.5%

URBAN CONDITIONS POPULATION:5 million TOTAL LOTS:317,865 L.BEST PERFORMANCE: 54,932

5PT (K-MEANS)

17.2%

URBAN CONDITIONS POPULATION:5 million TOTAL LOTS:27,615 L.BEST PERFORMANCE: 1,923

URBAN CONDITIONS POPULATION:5 million TOTAL LOTS:86,574 L.BEST PERFORMANCE: 2,529

6.9%

2.4%

5PT (K-MEANS)

me nu

CONCLUSION & NEXT STEPS

The workflow demonstrates the different limitations that tools can have when handling large data sets. Python and Osmx libraries open the way to manipulate large data sets that can benefit multiple urban communities. Opportunity to complement, improve and create new/existing large CONCLUSION CONCLUSION open-source datasets that can test and & NEXT & inform NEXT STEPS STEPS design processes. Google places

O P P O R T U N I T I E S

// OPPORTUNITIES // OPPORTUNITIES

+ COMPUTATION + COMPUTATION & BIG & DATA BIG DATA THE WORKFLOW THE WORKFLOW DEMONSTRATES DEMONSTRATES THE THE DIFFERENT DIFFERENT LIMITATIONS LIMITATIONS THAT THAT TOOLSTOOLS CAN HAVE HANDLING CAN HAVE WHEN WHEN HANDLING LARGELARGE DATA DATA PYTHON AND OSMX LIBRARIES SETS.SETS. PYTHON AND OSMX LIBRARIES THE TO WAYMANIPULATE TO MANIPULATE OPEN OPEN THE WAY LARGELARGE CAN BENEFIT DATA DATA SETS SETS THAT THAT CAN BENEFIT MULTIPLE COMMUNITIES MULTIPLE URBANURBAN COMMUNITIES

+ OPEN + OPEN SOURCE SOURCE DATASETS DATASETS OPPORTUNITY OPPORTUNITY TO COMPLEMENT, TO COMPLEMENT, IMPROVE IMPROVE AND CREATE AND CREATE NEW/EXISTING NEW/EXISTING LARGELARGE OPEN OPEN SOURCE SOURCE DATASETS DATASETS THAT THAT CAN BE CANUSE BE TO USETEST TO TEST AND INFORM AND INFORM DESIGN DESIGN PROCESSES. PROCESSES.

+ osm datasets are often driven by commercial applications, neglecting non-marketable areas and spaces that are still important for the city. The process allows users a new series of opportunities, but the question about real-case applications in the urban area remains. Who could benefit from this, and how can we make better cities with it.

// REFLEXIONS // REFLEXIONS

+ DATA + DATA GATHERING GATHERING GOOGLE GOOGLE PLACES PLACES + OSM+ DATASETS OSM DATASETS ARE OFTEN ARE OFTEN DRIVEN DRIVEN BY BY COMMERCIAL COMMERCIAL APPLICATIONS, APPLICATIONS, NEGLECTING NEGLECTING NON-MARKETABLE NON-MARKETABLE PLACES PLACES AND SPACES AND SPACES THAT THAT ARE ARE STILLSTILL IMPORTANT IMPORTANT FOR THE FOR CITY THE CITY

+ URBAN + URBAN APPLICATIONS APPLICATIONS THE PROCESS THE PROCESS ALLOWS ALLOWS USERSUSERS A NEWA NEW SERIES SERIES OF OPPORTUNITIES, OF OPPORTUNITIES, BUT BUT THE QUESTION THE QUESTION ABOUTABOUT REAL-CASE REAL-CASE APPLICATIONS APPLICATIONS ON THE ON URBAN THE URBAN SPACESPACE STILLSTILL REMAINS. REMAINS. WHO COULD WHO COULD BENEFIT BENEFIT FROM FROM THIS THIS AND HOW AND CAN HOW CAN WE MAKE WE MAKE BETTER BETTER CITIES CITIES WITH WITH IT. IT.

\\ A DATA DRIVEN APPROACH TO URBAN ACTIVATION

+LINK \\ A DATA DRIVEN APPROACH TO URBAN ACTIVATION

+ ADDING NEW AMENITIES AND SYNCING WITH GOOGLE SHEETS

Screenshots from webpage

MAPS UPDATED WITH NEW DATA SHOWING ACTIVATION RESULTS

02 01

USER ADDS NEW URBAN POINT

CALCULATE NEW SCORES AND CLUSTERS

https://city-lab.wixsite.com/urbanvoids

me nu

Training The Topology IAAC Spring 2021

+ Aleksander Mastalski + Andrei Okolokoulak

Faculty: Gabriella Rossi Asst: Iliana Papadopoulou http://www.iaacblog.com/programs/training-the-topology/

PROJECT ABSTRACT

TOPOLOGY OPTIMIZATION

With AI and machine learning invading the AEC industry, we are offered an opportunity to improve the performance of our existing tools. If trained well, the model can generate a prediction faster than results generated with a simulator. From previous experience, topology optimization tools like a Grasshopper 3D plugin t0pos or millipede can take a long time to compute geometries. We found 3D Convolutional Neural Networks (3DCNNs) to be an applicable solution. We looked at the 3D MNIST dataset of 3D handwritten numbers as a reference. The objective of this research is to apply machine learning methods to generate a visual prediction of [TOPOLOGY the topology-optimized geometry given the loads, support, OPTIMIZATION] and a volumetric bounding box.

Topology optimization is a mathematical method that optimizes material layout within a given design space, for a given set of loads, boundary conditions and constraints with the goal of maximizing the performance of the system. It takes a 3D design space and literally carves away material within it to achieve the most efficient design.

It takes a 3D design space and literally carves away material within it to achieve the most efficient design.

Source: Comsol

PROBLEM

[PROBLEM]

[SOLUTION]

Current tools like Grasshopper plugin t0pos, or millipede can be slow and require a lot of computing power, especially while dealing with multiple models at the same time.

Through ML, teach the

algorithm to generate Current tools like Grasshopper plugin visual predictions of topological simulations, t0pos, or millipede can be slow and reducing computing time. require a lot of computing power, especially while dealing with multiple models at the same time.

SOLUTION Through ML, teach the algorithm to generate visual predictions of topological simulations, reducing computing time.

AIA COMVISION

PSEUDOCODE 02

[PSEUDOCODE] [PARAMETERS]

[OUTPUT]

GEOMETRY 1 BOUNDING BOX

GEOMETRY 3

SUPPORT

DATASET For every iteration, Vectors (x,y,z values)

GEOMETRY 2

LOAD

[VARIATIONS]

[GEOMETRIES]

[INPUT]

PREDICTION

Prediction of xyz points on the map

VARIATION 1

LOAD

VARIATION 2 VOXEL THRESHOLD

The initial challenge was dataset [DATASET] preparation in grasshopper. We used DATASET t0pos with a fixed scale support and CREATION bounding box, while iterating the top layer (loads). We prepared two data[PART #1] sets: a less complex binary dataset In order to generate that only recognizes if there is a voxel In order to generate datasets for the datasets for the algo- or not, algorithm to learn from, first and it is a more elaborate data with rithm to learn from, first important to setitup the model for that maps the density of a gradient is important to set up the training. voxels. We randomly divided the datamodel for training. withfor a train split from Scikit-learn Establish setsset of rules to and have validation data. While • Establish setsgeometry of rulesinputs its more iterations. size) the model’s architecture, we for geometry(geometry inputs shape, creating and its iterations. (ge- both increased and decreased the Voxelize for a lighter ometry shape, size) geometries number machine learning binaryof MLP layers, switched berecognition tween 1 and 3 channels, tweaked hy• Voxelize geometries for a lighter machine perparameters, optimizers, learning learning binary rec- rate and tried completely different arognition chitectures.

[APPLICATION OF MACHINE LEARNING]

[PART #1]

PREPARING DATASETS

[PART #2]

TRAINING THE MODEL

1 The triangulated

WORKFLOW] SHOPPER WORKFLOW]

1. The triangulated mesh is mesh is aborted aborted for voxels as for it voxels is as it is more translatable to the al- translatable more gorithm for machine learnto the algorithm ing. for machine learning.

[GRASSHOPPER WORKFLOW]

04 2

2 2. Each vertical layer is 31 scanned and the colour Each vertical layerinto is scanned is translated number, vertical layer is scanned and the colour isEach translated into The which is laterandencoded intriangulated colour is translated number, which is laterthe encoded datasets by into mesh is aborted 3d+n-channelsnumber, array Get comisvoxels later encoded in 3d+n-channels array which iterating through the for as it is patible with the model. in 3d+n-channels array of compatible with the model. combinations more translatable Themodel model is represented bythe compatible with model. The is represented by 0 loads voxels. to the algorithm 0 and 1 or by gradient. The model is represented and 1 or by gradient. for machine by 0

DATASET CREATION

Improve learning overtime by using different model representations.

and 1 or by gradient. learning. [1

2] [1 [16 ]

2] [1

[GRASSHOPPER WORKFLOW]

04 4 1

Improve learning

0 00

0 0 01 02 0 10 00 0 20 00

2 3 4 03 04 05 1 3 00 10 10 1 11 21 20 1

5 0 00 11

0 1 2 0 01 02 1 03 2 04 1 2 0 10 1 00 2 00 21 1 0 20 11 21 31

15 1 0 10 1 2 30 2 1

5 0 00

0 1

0 1 2

1 0

0 1

0 1 2

1 2

0 1

Improve learning overtime by The triangulated 3. 2 3 3 3 0Get 1 1datasets 0 0 02 0by 3 0 iterating 0 1 1 1 2 0 1 0 1 02 0 3 1 Each 1 2 2 2vertical 31 2 1 1 layer 1 1 2 02 is 1scanned by the combinations mesh is aborted using different overtime datasets through 3 0 4 1 0 1 1 0Get 0 of 0 1 03 0 by 1 and 2 2the 1 2 colour 1 2 0 1 is 0translated 3 1 2 3into 1 2 4 0 0 1 0 0 0 2 2 4 1 formodel voxels as it is which is later encoded using different iterating throughnumber, the loads 1 2 2 2 5 0 0 voxels. 0 1 0 04 0 5 0 0 1 0 0 1 0 2 0 1 04 0 5 1 in 0 23d+n-channels 1 0 2 1 1 2 0 1 04 array 1 more translatable representations. model combinations of 0 1 0 2 10 1 1 0with 0 1 1 0 1 2 to the algorithm 6 0 0 0 1 0 05 0 6 0 0 0 0 1 0 0 1 0 1 05 0 6 0 compatible 1 the 05 model. representations. loads voxels. for machine 1 01 1 0 represented 0 01 1 7 0 0 0 0 0 06 0 7 0 0 0 0 1 0 0 0 0 0 06 0 7 0 The 0 0 0model 0is 0 06 0 0 by learning. and 1 or by gradient.

Get datasets by iterating through the combinations of loads voxels.

4 Improve learning 4. Improve learning overtime overtime by by using different model using different representations. model representations.

0 1

Improve learnin overtime by using different model representations 2] [1

[16 ]

0 1

0 1 2

1 0

0 1

0 1 2

1 2

0 1

GOOGLE C O L A B WORKFLOW

[IMPORT DATASET]

[PREPARE DATA I]

[PREPARE DATA II]

[CREATE MODEL ARCH]

[ADD 2D CONV OUTPUT]

8200 models of 16x16x16 resolution. Voxel density value is imported from t0pos, train/test split 0.2. Divide dataset into: x-model y-load

Use cm.ScalarMappable to convert 1 channel model to 3 channel gradient representation or... Use single channel density gradient model: x-train,x-test shape:(16, 16, 16, 1)

Prepare y-train and y-test: reshape to match output 2d convolution. shape:(16, 16, 1)

1) Original 3dMnist example 2) Increased MLP layers 3) Reduced MLP layers, single channel. 4) Single conv2d output 5) 3d conv to 2d conv arch. single channel.

MLP architecture with dense layers 4096 - 512 - 256

[ITERATE]

[SAVE PREDICTION]

[ANALYZE MODEL]

[COMPILE MODEL]

[3D CONV INTO 2D CONV]

Moving forward.

Reshape prediction into 1D. Create a dataset for Grasshopper.

Summarize history of loss and accuracy

Total params: 134,417. # of epochs: 100 Batch size:50 Optimizer: Adadelta Learning rate: 0.01

Prepares the readable dataset



del

g ted t e.

MODEL A [DATASET SHAPE: x(4096, 16, 12, 20) y(4096, 16, 12)]

This was our starting point. Model A’s strategy was a more simple, binary approach to voxel’s presence recognition. The initial architecture is copied after the 3D MNIST DATA. It’s initial setup was proven to be the best. For Model A, we have explored the impact of changing hyperparameters as well as tested various optimizers to see how it affects the model’s performance. Later on we also explored the change

MODEL A.1.2

MODEL A.1.2 BEST PERFORMANCE IN A

MODEL A.1.2

Array that was given as an output was in range 1.2908e-8 to 0.207374, by truncating the values with the threshold of 0.0001. The actual loads were chosen. Index of model [0]

MODEL A.1.2 loss=binary_crossentropy optimizer=Adadelta learning_rate=0.01 metrics=['acc']

history = model.fit(x=xtrain, y=y_train, batch_size=50 epochs=500 (+350) validation_split=0.1) loss=binary_crossentropy optimizer=Adadelta learning_rate=0.01 metrics=['acc']

●

Adadelta optimization is a stochastic

●

Adadelta optimization is a stochastic The need for a manually selected global learning ○ rate. gradient descent method that is based on Using pyplot cm scalar map=’oranges’ adaptive learning rate per dimension toto generate 3 channels address two drawbacks:

historygradient = model.fit(x=xtrain, y=y_train, descent method that is based on batch_size=50 adaptive learning epochs=500 (+350)rate per dimension to validation_split=0.1) address two drawbacks: ○

●

○ ○ ○

○

●

The continual decay of learning rates throughout training.

A mixin class to map scalar data to RGBA. The continual decay of learning rates throughout The ScalarMappable applies data training. normalization before returning RGBA colors The need for a manually selected global learning from the given colormap.

red dots - Predicted loads [model.predict(xtest)] grey dots - Actual model [x_test]

Array that was given as an output was in range Model: "model_1" 8.7723e-8 to 0.072938, by truncating the values _________________________________________________________________ Layer (type) Output Shape # with the threshold of 0.0001. The actual Param loads ================================================================= input_2 (InputLayer) [(None, 12, 16, 20, 3)] 0 were chosen. Index of model [12] _________________________________________________________________

FLAWLESS GEOMETRIC RESULTS AFTER APPLYING THRESHOLD!!!

Array that was given as an output was in range 1.2198e-8 to 0.039548, by truncating the values with the threshold of 0.0001. The actual loads were chosen. Index of model [202]

conv3d_4 (Conv3D) (None, 12, 16, 20, 8) 656 _________________________________________________________________ conv3d_5 (Conv3D) (None, 12, 16, 20, 16) 3472 _________________________________________________________________ max_pooling3d_2 (MaxPooling3 (None, 6, 8, 10, 16) 0 _________________________________________________________________ Model: "model_1" conv3d_6 (Conv3D) (None, 4, 6, 8, 32) 13856 _________________________________________________________________ _________________________________________________________________ Layer (type) Output 2, Shape Param # conv3d_7 (Conv3D) (None, 4, 6, 64) 55360 ================================================================= _________________________________________________________________ input_2 (InputLayer) [(None,1,12, 20, 3)] max_pooling3d_3 (MaxPooling3 (None, 2, 16, 3, 64) 00 _________________________________________________________________ _________________________________________________________________ conv3d_4 (Conv3D) (None, 1, 12,2,16, 656 batch_normalization_1 (Batch (None, 3, 20, 64) 8) 256 _________________________________________________________________ _________________________________________________________________ conv3d_5 (Conv3D) (None, 384) 12, 16, 20, 16) flatten_1 (Flatten) (None, 03472 _________________________________________________________________ _________________________________________________________________ max_pooling3d_2 (None, 3840) 6, 8, 10, 16) 0 dense_4 (Dense) (MaxPooling3 (None, 1478400 _________________________________________________________________ _________________________________________________________________ conv3d_6 (Conv3D) (None, 3840) 4, 6, 8, 32) dropout_3 (Dropout) (None, 013856 _________________________________________________________________ _________________________________________________________________ conv3d_7(Dense) (Conv3D) (None, 512) 2, 4, 6, 64) 55360 dense_5 (None, 1966592 _________________________________________________________________ _________________________________________________________________ max_pooling3d_3 (MaxPooling3 (None, (None, 512) 1, 2, 3, 64) dropout_4 (Dropout) 00 _________________________________________________________________ _________________________________________________________________ batch_normalization_1 (Batch (None, (None, 512) 1, 2, 3, 64) dropout_5 (Dropout) 0256 _________________________________________________________________ _________________________________________________________________ flatten_1 (Flatten) (None, 192) 384) 0 dense_7 (Dense) (None, 98496 _________________________________________________________________ ================================================================= dense_4 (Dense) (None, 3840) 1478400 Total params: 3,617,088 _________________________________________________________________ Trainable params: 3,616,960 dropout_3 (Dropout) (None, 3840) 0 Non-trainable params: 128 _________________________________________________________________ dense_5 (Dense) (None, 512) 1966592 _________________________________________________________________ dropout_4 (Dropout) (None, 512) 0 _________________________________________________________________ dropout_5 (Dropout) (None, 512) 0 _________________________________________________________________ dense_7 (Dense) (None, 192) 98496 ================================================================= Total params: 3,617,088 Trainable params: 3,616,960 Non-trainable params: 128

rate.

Using pyplot cm scalar map=’oranges’ to generate 3 channels ○ ○

A mixin class to map scalar data to RGBA. The ScalarMappable applies data normalization before returning RGBA colors from the given colormap.

[MODEL B] MODEL B

DATASET SHAPE x(8200, 16, 12, 20 y(8200, 16, 16

MODEL B

DATASET SHAPE x(8200, 16, 16, 16 y(8200, 16, 16 s B we explore the on of voxels into a d gradient number

MODEL B [DATASET SHAPE: x(8200, 16, 16, 16) y(8200, 16, 16)]

dels yielded poor results. s B we explore the visual prediction and the on of voxels into a , loss values suggested d gradient number. approach needed In models B we explore of [MODEL B] 11 the conversion ering. dels yieldedvoxels poor results. into a remapped gradient DATASETnumber. SHAPE visual prediction and the x(8200, 16, 12, 20 , loss values suggested y(8200, 16, 16 approach needed Both models yielded poor results. Both ering. DATASET SHAPE

MODEL B [MODEL B

16, 16, 16 the visual prediction andx(8200, the accuracy, y(8200, 16, 16

In models we explore the loss values suggested that Bthe approach conversion of voxels into a

remapped gradient number needed reconsidering.

[MODEL B]

MODEL B DATASET SHAPE x(8200, 16, 16, 16 y(8200, 16, 16

Both models yielded poor results. In models B we explore the Both the visual prediction and the conversion of voxels into a accuracy, loss values suggested remapped gradient number. that the approach needed reconsidering. Both models yielded poor results. Both the visual prediction and the accuracy, loss values suggested that the approach needed reconsidering.

Reshaped MLP layer and outputting 2D Conv layer.

B.1

dels B we explore the rsion of voxels into a ped gradient number.

16x16x16 model, models yielded poor results. he visual prediction and3the channels representacy, loss values suggested ed by the remapped 161616 model, he approach needed 3 channels density gradient from sidering. represented by the 0 to 1.

Reshaped MLP layer and outputting 2D Conv layer.

remapped density gradient from 0 to 1.

B.2

MODEL B.1

MODEL B.1 BEST PERFORMANCE IN B

red dots - Predicted loads [model.predict(xtest)] grey dots - Actual model [x_test]

MODEL B.1 MODEL B.1

Array that was given as an output was in range 0.00164 to 0.00674 by truncating the values with the threshold of 0.0037. The actual loads were chosen. Index of model [0]

loss=binary_crossentropy optimizer=Adadelta learning_rate=0.01 metrics=['acc']

history = model.fit(x=xtrain, y=y_train, batch_size=50 loss=binary_crossentropy epochs=100 optimizer=Adadelta validation_split=0.1) learning_rate=0.01 metrics=['acc']

Adadelta optimization is a stochastic

●

history = model.fit(x=xtrain, y=y_train, gradient descent method that is based on batch_size=50 epochs=100 adaptive learning rate per dimension to validation_split=0.1)

●

address two drawbacks: ○ The continual decay of learning rates throughout Adadelta optimization is a stochastic training. gradient method that is based on ○ descent The need for a manually selected global learning rate. adaptive learning rate per dimension to ● address Using two pyplot cm scalar map=’oranges’ to drawbacks: generate channels ○ The3continual decay of learning rates throughout ○

○○

● ●

input_4 (InputLayer) [(None, 16, 16, 16, 3)] 0 _________________________________________________________________ conv3d_12 (Conv3D) (None, 14, 14, 14, 8) 656 _________________________________________________________________ conv3d_13 (Conv3D) (None, 12, 12, 12, 16) 3472 _________________________________________________________________ max_pooling3d_6 (MaxPooling3 (None, 6, 6, 6, 16) 0 _________________________________________________________________ Layer (type) Output Shape Param # conv3d_14 (Conv3D) (None, 4, 4, 4, 32) 13856 ================================================================= _________________________________________________________________ input_4 (InputLayer) [(None, 16, 16, 16, 3)] 0 conv3d_15 (Conv3D) (None, 2, 2, 2, 64) 55360 _________________________________________________________________ _________________________________________________________________ conv3d_12 (Conv3D) (None, 14, 14, 14, 8) 656 max_pooling3d_7 (MaxPooling3 (None, 1, 1, 1, 64) 0 _________________________________________________________________ _________________________________________________________________ conv3d_13 (Conv3D) (None, 12, 12, 12, 16) 3472 batch_normalization_3 (Batch (None, 1, 1, 1, 64) 256 _________________________________________________________________ _________________________________________________________________ max_pooling3d_6 (MaxPooling3 (None, 6, 6, 6, 16) 0 flatten_3 (Flatten) (None, 64) 0 _________________________________________________________________ _________________________________________________________________ conv3d_14 (Conv3D) (None, 4, 4, 4, 32) 13856 dense_11 (Dense) (None, 4096) 266240 _________________________________________________________________ _________________________________________________________________ conv3d_15 (Conv3D) (None, 2, 2, 2, 64) 55360 dropout_9 (Dropout) (None, 4096) 0 _________________________________________________________________ _________________________________________________________________ max_pooling3d_7 (MaxPooling3 (None, 1, 1, 1, 64) 0 dense_12 (Dense) (None, 512) 2097664 _________________________________________________________________ _________________________________________________________________ batch_normalization_3 (Batch (None, 1, 1, 1, 64) 256 dropout_10 (Dropout) (None, 512) 0 _________________________________________________________________ _________________________________________________________________ flatten_3 (Flatten) (None, 64) 0 dropout_11 (Dropout) (None, 512) 0 _________________________________________________________________ _________________________________________________________________ dense_11 (Dense) (None, 4096) 266240 dense_14 (Dense) (None, 256) 131328 _________________________________________________________________ ================================================================= dropout_9 (Dropout) (None, 4096) 0 Total params: 2,568,832 _________________________________________________________________ Trainable(Dense) params: 2,568,704 (None, 512) dense_12 2097664 Non-trainable params: 128 _________________________________________________________________

Array that was given as an output was in range 0.001572 to 0.016214, by truncating the values with the threshold of 0.0042. The actual loads were chosen. Index of model [1257]

dropout_10 (Dropout) (None, 512) 0 _________________________________________________________________ dropout_11 (Dropout) (None, 512) 0 _________________________________________________________________ dense_14 (Dense) (None, 256) 131328 ================================================================= Total params: 2,568,832 Trainable params: 2,568,704 Non-trainable params: 128

from the given colormap.

Using pyplot cm scalar map=’oranges’ to Using 16x16x16 model represented by the generate 3 channels density gradient from 0 to 1. data to RGBA. ○ A mixin class to map scalar ○

●

A mixin class to map scalar data to RGBA. training. Theneed ScalarMappable applies data The for a manually selected global learning normalization before returning RGBA colors rate.

Array that was given as an output was in range 0.001329 to 0.00931, by truncating the values with the threshold of 0.0036. The actual loads were Layer (type) Output Shape Param # ================================================================= chosen. Index of model [524]

The ScalarMappable applies data normalization before returning RGBA colors from the given colormap.

Using 16x16x16 model represented by the density gradient from 0 to 1.

MODEL C

[MODEL C]

[DATASET SHAPE: x(8200, 16, 16, 16) y(8200, 16, 16)]

[MODEL C DATASET SHAPE x(8200, 16, 12, 20 y(8200, 16, 16

In this model we developed a new architecture. Same as at the end of In this model we developed a new model B, Same weasmaintain the 16x16x16 architecture. at the end of model B, we maintain the model 161616represented model representedby by aa density gradensity gradient from 0 to 1, dient from 0 to 1, however now we however now we are reshaping the model from a 3D convolution to a are reshaping the model from a 3D 2D convolution and reducing the convolution number of filters to in 2Da 2D convolution and convolutional layers. reducing the number of filters in 2D At this point we were able to convolutional layers. generate a result that satisfies us andwe mathematically Atboth thisvisually point were able to generate with a high accuracy and low loss. a result that satisfies us both visually and mathematically with a high accuracy and low loss. 12

[MODEL C]

[MODEL C DATASET SHAPE x(8200, 16, 12, 20 y(8200, 16, 16

In this model we developed a new architecture. Same as at the end of model B, we maintain the 161616 model represented by a density gradient from 0 to 1, however now we are reshaping the model from a 3D convolution to a 2D convolution and reducing the number of filters in 2D convolutional layers. At this point we were able to generate a result that satisfies us both visually and mathematically with a high accuracy and low loss.

Using new archiUsing new cm tecture,architecture, with with cm remapping and 3 scalar scalar remapping channel conv3d and 3 channel conv3d

Using new architecture, without cm scalar remapping and with 1 channel conv3d layers, Using new architecture, without Filters 2d andconv cm scalarin remapping with 1 channel conv3d layers, layerFilters are in 2dgradually conv layer are gradually decreasing. decreasing.

Using new archiUsing new architecture, without tecture, without cm cm scalar remapping and with 1 channel scalar remapping conv3d layers and with 1 channel conv3d layers C.1

C.1.2 BEST

C.1.3

C.2 WORST

Using same parameters as in model C.1, but the actual model is not Using samerepresented parameters as in model C.1, but the actual inmodel gradient before is not represented in gradient before using using cmscalar, cmscalar, but in 0 and 1. but in 0 and 1.

MODEL C.1.2

MODEL C.1 BEST PERFORMANCE OF ALL

Array that was given as an output was in range -0.280566 to 0.9603 by truncating the values with the threshold of 0.67. The actual loads were chosen. Index of model [0]

MODEL C.1.2 MODEL C.1.2

loss=binary_crossentropy optimizer=Adadelta learning_rate=0.01 metrics=['acc'] loss=binary_crossentropy optimizer=Adadelta history = model.fit(x=xtrain, y=y_train, learning_rate=0.01 batch_size=50 metrics=['acc'] epochs=100 validation_split=0.1) history = model.fit(x=xtrain, y=y_train, batch_size=50 ● Adadelta optimization is a stochastic epochs=100 validation_split=0.1) gradient descent method that is based

●

● ● ● ● ● ●

on adaptive learning rate per dimension to Adadelta optimization is a stochastic address two drawbacks: gradient descent method that is based on ○ The continual decay of learning rates throughout adaptive learning rate per dimension to training. ○ The need for a manually selected global learning address two drawbacks: ○

rate. The continual decay of learning rates throughout

Using 16x16x16 training. model represented by the ○ gradient The need from for a manually density 0 to 1.selected global learning rate. Using single channel 3dConv Using 16x16x16 model represented by the Reshaping model from 3d conv to 2d conv density gradient from 0 to 1. Using single channel 3dConv Reshaping model from 3d conv to 2d conv

red dots - Predicted loads [model.predict(xtest)] grey dots - Actual model [x_test]

Array that was given as an output was in range -0.197632 to 0.995739, by truncating the values with the threshold of 0.29. The actual loads were chosen. Index of model [524]

Array that was given as an output was in range -0.263189 to 0.996837, by truncating the values with the threshold of 0.2852. The actual loads were chosen. Index of model [1257]

Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 16, 16, 16, 1)] 0 _________________________________________________________________ conv3d (Conv3D) (None, 14, 14, 14, 8) 656 _________________________________________________________________ Layer (type) Output Shape Param # conv3d_1 (Conv3D) (None, 12, 12, 12, 16) 3472 ================================================================= _________________________________________________________________ input_1 (InputLayer) [(None, 16, 16, 16, 1)] 0 max_pooling3d (MaxPooling3D) (None, 6, 6, 6, 16) 0 _________________________________________________________________ _________________________________________________________________ conv3d (Conv3D) (None, 14, 14, 14, 8) 656 conv3d_2 (Conv3D) (None, 4, 4, 4, 32) 13856 _________________________________________________________________ _________________________________________________________________ conv3d_1 (Conv3D) (None, 12, 12, 12, 16) 3472 conv3d_3 (Conv3D) (None, 2, 2, 2, 64) 55360 _________________________________________________________________ _________________________________________________________________ max_pooling3d (MaxPooling3D) (None, 6, 6, 6, 16) 0 max_pooling3d_1 (MaxPooling3 (None, 1, 1, 1, 64) 0 _________________________________________________________________ _________________________________________________________________ conv3d_2 (Conv3D) (None, 4, 4, 4, 32) 13856 batch_normalization (BatchNo (None, 1, 1, 1, 64) 256 _________________________________________________________________ _________________________________________________________________ conv3d_3 (Conv3D) (None, 2, 2, 2, 64) 55360 flatten (Flatten) (None, 64) 0 _________________________________________________________________ _________________________________________________________________ max_pooling3d_1 (MaxPooling3 (None, 1, 1, 1, 64) 0 reshape (Reshape) (None, 1, 1, 64) 0 _________________________________________________________________ _________________________________________________________________ batch_normalization (BatchNo (None, 1, 1, 1, 64) 256 conv2d_transpose (Conv2DTran (None, 2, 2, 32) 18464 _________________________________________________________________ _________________________________________________________________ flatten (Flatten) (None, 64) 0 conv2d_transpose_1 (Conv2DTr (None, 4, 4, 32) 9248 _________________________________________________________________ _________________________________________________________________ reshape (Reshape) (None, 1, 1, 64) 0 conv2d_transpose_2 (Conv2DTr (None, 8, 8, 32) 9248 _________________________________________________________________ _________________________________________________________________ conv2d_transpose (Conv2DTran (None, 2, 2, 32) 18464 conv2d_transpose_3 (Conv2DTr (None, 16, 16, 32) 9248 _________________________________________________________________ _________________________________________________________________ conv2d_transpose_1 (Conv2DTr (None, 4, 4, 32) 9248 conv2d (Conv2D) (None, 16, 16, 1) 289 _________________________________________________________________ ================================================================= conv2d_transpose_2 (Conv2DTr (None, 8, 8, 32) 9248 Total params: 120,097 _________________________________________________________________ Trainable params: 119,969 conv2d_transpose_3 (Conv2DTr (None, 16, 16, 32) 9248 Non-trainable params: 128 _________________________________________________________________ conv2d (Conv2D) (None, 16, 16, 1) 289 ================================================================= Total params: 120,097 Trainable params: 119,969 Non-trainable params: 128

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[FURTHER STEPS] STEPS] 13[FURTHER

FURTHER STEPS

Predict th Pr model based mod load notloa th load based load model

CURRENT CURRENT PROGRESS PROGRESS

100

Apply 3D CNN to other architectural problems that reApply 3D CNN to other quire reading voxarchitectural problems that el vector.

TEPS]

require reading voxel vector.

OTHER APPLICATION

Predict the model based on load not the load based on model

TWEAK MODEL FURTHER

REVERSE MODEL

INCREASE DATASET

Play more with: + filters + activations + optimizers + model representations + reduce conv2d overfittingmore with: Play + approach GAN archi+ filters + activations tecture

+ optimizers + model representations + reduce conv2d overfitting + approach GAN architecture

Iterate the support Iterate the support and bounding box and bounding box geometries.

geometries.

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Architectural Intermediates [thesis project] IAAC 2021

+ Aleksander Mastalski + Krishnanunni Vijayakumar

Faculty: Oana Taut Neural Network or other M.L applications are so integrated into our daily life, that we hardly notice it. These technologies focus on automating the work we do manually, so that we can shift our focus towards more sensible and stimulating subjects.

The aim of this paper is to apply machine learning techniques to develop design tools that could deal with geometric data so as to traverse the creative expanse of the design field. Main aspects of the work are as follow.

Thesis timeline

This is the same in Architecture where there is a lot of work done in using machine learning techniques to deal with more basic tasks while the designer get more time for creative expression.

This process enables us to use our brain’s capabilities to intuitively express designs and 3d forms as well as expand on it. And this lead us the question of how can we recreate this capability of creative expansion using NN?

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Objectives

To explore the potential of 3DGAN NNs in architectural design as an efficient method in generating new design solutions.

To unveil the possible use of latent space operations as a design methodology.

To identify the directions in which this methodology can be taken forward as a new creative design tool.

The focus of this project is to inspect and investigate the possible use of 3DGAN to generate novel architecture by using existing architectural 3d data and to devise a formal workflow with which the method can be used as an architectural design intervention.

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Project Workflow

The project followed a workflow involving curation and preparation of 3d datasets, data conversions, and many iterations of training. Multiple models where tested out with different runs for each with varying hyper parameters until the best combination was achieved.

[PREPARE DATASET]

[PREPARE DATA]

[STATE OF THE ART MODEL ARCH]

[TEST 1]

Scrape 3d models from internet, refine and voxelize using automated grasshopper script and save to CSV. Models’ voxel resolution: 643 and 323

Load the CSV into numpy, reshape it and bulk save to the *.npy files

1) Wu, Zhang, Xue, Freeman, Tenenbaum (2016) „Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling“ 2) rp2707, Greg K (wasd12345) (2017) „coms4995-project“ 3) Smith, Meger (2017) „Improved Adversarial Systems for 3D Object Generation and Reconstruction“ 4) Wiegand (2018) „Eine Einführung in Generative Adverserial Network(GAN)“ 5) Our own approach to creating the model based on projects above.

Testing the Keras implementation of the MIT model, which ended up with the mode collapse. [model 1]

[ITERATE]

[FURTHER DEVELOPMENT]

[INTERPOLATION]

[TEST 3]

[TEST 2]

In order to get most meaningful results we tested variety of different tools including vector interpolation, vector arithmetics, learning from checkpoints and transfer learning.

After success with the resolution of 323 we decided to further develop tensorflow 1 architecture by increasing the resolution to 643 and training the model further. [model 4, res: 643]

The outputs of the last test were satisfactory on various different datasets and thus we performed interpolation.

Testing the Tensorflow 1 implementation based on 2nd reference, which uses Wasserstein distance normalization with gradient penalization as a training objective to generate more differentiated outputs [model 3, res: 323]

Testing the Tensorflow 1 implementation of the MIT model, based on coms4996 project, which also tended to collapse and result in unpredictable outputs. [model 2]

Dataset creation

Model 1

Wu, Zhang, Xue, Freeman, Tenenbaum (2016) „Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling“ [NIPS]

Keras implementation of the architecture suggested in paper. IKEA dataset

The first run involved a keras implementation of the architecture suggested in the MIT paper[5]. This model was ran on two different dataset; IKEA furniture dataset and a column capital dataset. With the use of matplotlib libraries in python we were able to visualize the output based on the values predicted, thus giving a deeper understanding of the performance of the model and the output generations. The outputs were not refined but the model was still able to generate capitals and tables that were distinguishable.

Columns dataset

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Model 2

[BASED ON] rp2707, Greg K (wasd12345) (2017) „coms4995-project“ Mode collapse occurred again.

The second model used was a Tensorflow 1 version of the same architecture. Although this model was more refined the outputs were still not showing any signs of progress. Furthermore the two networks ran into mode collapse and the network was unable to generate variations. This was vital for the project as latent space operations required the model to generate different outputs.

Mode collapse

“Mode collapse occurs when the generator produces an especially plausible output, the generator may learn to produce only that output. In fact, the generator is always trying to find the one output that seems most plausible to the discriminator.”

Wasserstein method

“Wasserstein GAN, or WGAN, is a type of generative adversarial network that minimizes an approximation of the Earth-Mover’s distance (EM). It leads to more stable training than original GANs with less evidence of mode collapse.”

With the use of the Wasserstein method the GAN model can be optimized so that the output generations have variations. The model trains to optimize the weights so that it deviates from generating single solutions.

3DIWGAN model

Source: Improved Adversarial Systems for 3D Object Generation and Reconstruction Edward J. Smith , David Meger Department of Computer Science McGill University Canada

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(image:Two one-dimensional distributions ,

Model 3 IWGAN Run 1

Smith, Meger (2017) „Improved Adversarial Systems for 3D Object Generation and Reconstruction“ & Wiegand (2018) „Eine Einführung in Generative Adverserial Network(GAN)“

Model 3 was a 3D IWGAN written using tensorflow 1 library. The model was set up for 323 voxel resolution inputs. With the new model setup a new dataset was introduced. This included 3d geometry of churches that was sourced from thingiverse - an online repository for 3d geometries for printing. The churches provided a dataset that is large enough but also has recognizable features even in smaller resolutions.

Further developed Tensorflow 1 implementation of architecture used in the NIPS paper with gradient penalty and Wasserstein method applied during loss calculations. The first set of iterations was based on the parameters from the original 3DGAN paper[5]. The outputs were rather poor, with the generations retaining the massing features but was missing the finer architectural details.

Clean meshes [Thingverse]

Voxelized [32, 32, 32]

Churches dataset 0-1500 epochs 107

Model 4 IWGAN Run 2

Smith, Meger (2017) „Improved Adversarial Systems for 3D Object Generation and Reconstruction“ & Wiegand (2018) „Eine Einführung in Generative Adverserial Network(GAN)“

Model 4 - an update to the 3D IWGAN was done to accommodate for improved dataset of resolution 64 x 64 x 64. This meant adding additional hidden layers at the output of the generator and the input of the discriminator to reshape the generated sample. For this a new dataset of churches in the higher resolution of 643 was made . The first run with this model was done with the same parameters as of the 3DGAN[5] paper and as expected the model had the tendency to leave out finer details. This might be due to the fact that the parameters used in the paper were optimal for generating furniture and other objects

Voxelized [32, 32, 32]

Voxelized [64, 64, 64]

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with larger and more distinguishable details. This meant the model required more fine tuning through trial and error until an ideal region is identified with the parameters. By increasing the learning rates, we observed that the model started learning more and output more detailed. Unfortunately, at higher epochs the loss values started to climb up and the GAN model became unstable again.

Churches dataset 0-1500 epochs

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Model 4 IWGAN Run 3

After a number of iterations an optimal range was reached within which the model kept improving with more training. The loss values kept reducing and the outputs showed more positive results. With this, the process was continued on an extended dataset and 11 rotations per each geometry in the input dataset is introduced. New generations showed more variations with more mix of features.

Churches dataset 0-5000 epochs

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Model 4 IWGAN NN architecture

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Model 4 IWGAN Run 4

With the optimal parameters at high epochs we were able to generate new church geometries that had unique features which were initially not seen in the input dataset. This showed great potential of generating new geometries based on existing 3d geometry and this could be extended and built upon to define many new design methodologies.

Churches dataset 0-5000 epochs

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Model 4 IWGAN Run 5

For the latent space operations to be more appropriate it was one of the priorities to be able to map the inputs in the latent space. This could enable us to develop a method to input 3d geometry and do latent vector operations on it.

Image Source ; embedGAN : A Method to Embed Images in GAN Latent Space by Zhijia Chen, Weixin Huang, Ziniu Luo

Many papers such as the embedGAN[8] addresses this issue and the solution is to further optimize the random latent input vector to reduce the distance between the inputs and the generations. With this approach we ran further iterations and were able to generate geometries with features almost identical to that of the input dataset. Here on the figure to the left we can see the generations similar to the dataset and as you move further to the right more features and unique solutions can be observed.

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GENERATED GEOMETRIES

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Latent Vector Interpolation

This enabled for a latent space interpolation between inputs as well as new generations. A latent space can be imagined as a 3D space which represents a distribution on to which the generations are mapped. Two points in this distribution represents two generations and a path between the two will mark the transition between two geometries.

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By moving through this path we can generate all geometries between these two points. A spherical linear interpolation is showcased on the right between the selected generations. This is exciting as this method, with sufficient data offers a possibility to find the intermediated between any two geometries which have completely different shape, size and topology.

Latent Space Walk

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Vector Arithemetic

Vector arithmetic is another possibility of the exploration of the latent space, where we can identify features within the distribution by calculating mean vectors of the geometries with these similar features. The resultant of these vector arithmetic can then be used to create new generations with such features. This is an interesting idea, as it allows for the intuitive and targeted generation.

Feature ; Central Tower + Regular Base

Feature ; Regular Base

Feature ; Two End Towers

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Use Case I

As the last step of the explorations the approach followed was to identify a small test dataset of architectural data. We chose buildings designed by ZHA in this case and prepared a dataset of recreated models from sketchup warehouse and used it to embed into latent space to perform interpolations. Though the outputs of the interpolations were very crude, this still

122

Central Bank of Iran Baghdad

Galaxy SOHO Beijing

Riverside museum Glasgow

Wohanhaus C Vienna

showed signs of promise. We could observe some intermediates between different buildings in the latent spacewalk. Considering the fact that we used 8 models that are very distinct, we are sure that with higher resolution and with a large enough dataset like what is available for any architectural practice this method has great value and potential.

Riverside museum Glasgow

Galaxy SOHO Beijing

Phaeno Science Center

Galaxy SOHO Beijing

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3D Reconstruction

Image

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An extension of this can be a 2D- 3D reconstruction as in the mentioned paper. This method uses paired dataset like a 2D DCGAN and therefore can be made into a 3DCGAN, which has high application in the field of design.

Image taken from paper.

Future steps

Smith, Meger „Improved Adversarial Systems for 3D Object Generation and Reconstruction“

Image taken from paper.

Decor - GAN

Zhiqin Chen, Vladimir G. Kim, Matthew Fisher, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri “3D Shape Detailization by Conditional Refinement” Decor - GAN

Decor GAN could be another extension where the low resolution outputs can be upsampled into higher resolution. This also offers the possibility to tune the upscale and meshing based on the dataset the Decor GAN is trained on and thereby opens the door to style transfer and similar methods.

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Conclusion

The combined workflow will enable new opportunities in the field of design. The reconstruction phase can be altered to input any data that can be embeded in a 2d image format and this will enable us to create generations in a more controlled manner. Thus, creating a tool with which the architect or the designer has the role of initiating the creation but with the neural network will be able to generate solution much beyond what they can create themselves. Further improvements in the direction of 3d generative neural networks can help open up new expanse in the field of design.

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Bachelor Thesis: Office building with commercial function in Lodz. Lodz University of Technology Professor Anetta KepczynskaWalczak

Lodz is a fast developing city which attracts rising number of new investments. The proposed office building with commercial function aims to be part of mentioned growth. This thesis was designed using new tools that are more and more accessible to architects to make their work easier, faster and more efficient. Drawings and model were designed using BIM software with aid of Grasshopper for parametric design and Ladybug for analysis that helped in shaping the building.

Daily(top), yearly(down) sun radiation analysis

Influence on the neighbor considering the shape of the designed building including sun radiation (top) and sun hours (bottom)

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Sun radiation and sun hour analysis were made to understand the impact of natural lighting and to maximize solar benefit in energy consumption of the building throughout the year. Their aim was also to analyze the influence of designed building on its surrounding.

Process of shaping the building started with analyzing the city regulations for chosen plot. According to guidelines 100% of the site had to be used (I), what is more, the western part had to be the highest part with different elevation values (II). In order not to strip the inhabitants of the neighboring estate from the access to natural lighting and according to the analysis, part of building was removed and connected with transparent skywalk (III-V). After those steps initial shape was achieved (V).

III

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Shaping of the parametric facade

Design decisions regarding facade were inspired by Lodz industrial history. The idea was to create new architecture that is not fully detached from the original context. Steel frames with bricks in between were commonly used in factorial facilities abundantly spread across the city. Proposed way of merging it into modern architecture consisted of using Grasshopper to create parametric facade. Having analysis in mind it was optimized to provide sufficient daylight to neighboring building. Full brick panels were used to maximize sun radiation benefit by blocking part of negative sun rays. After design phase was finished, facade was converted into BIM model using Archicad and Grasshopper Live Connection tool in order to generate complete model.

130

131

88 ,3 2°

0°

,4 7

Ground floor plan 1:200

88 ,3 2°

P-2

2 2175

0°

754

91 ,6 8°

243

403 Wentylatornia 0.7 16,8 m2

100 205

1775

P-1

Hall 0.2 44,1 m2

Pion instalacyjny

193

150

110 205

130

166

90 205

105

133

1815

90 205

359

252 27 x 17 x 25,9

Przeds WC 0.10 5 m2

Pion obsługujący wentylację mechaniczną

117

100

WC męskie 0.11 9,2 m2

665

1505

1 0985

166

276

525

300

540

290 230

100 1265 Zaplecze recepcji 0.5 10,4 m2

111

492

1515

151

Łazinka pracownicza 0.6 4,3 m2

Winda samochodowa 0.4 27,4 m2

90 205

288

144

1685

Ground floor consists of entrance area, two automated car park entrances, reception, stairwell and restaurant.

Pion instal

91 ,6 8°

332

293

555

5035

666

405

209.16 9

295

Komunikacja 0.3 11 m2

100

2845

120

665

13 14 15 16 17

428

8°

192 21 22

307

1115

Przegroda o odporności ogniowej minimum E I 30

24 27

,6 8

210

30 5

6 28

690

1. wyjścia ewakuacyjne: otwierane automatycznie i rę bez możliwości ich blokow samoczynnie rozsuwające pozostające w pozycji otwar wyniku zasygnalizowania p przez system wykrywania chroniący strefę pożarową ewakuacji z której te drzw przeznaczone, a także przypadku awarii drzwi.

6 28

855 20 3

210.71

105

1195

46 55

0 30

210.49

5 28

1 37

209.89

209.91

328

105

Wiatrołap 0.1 17,1 m2

5 28

209.82

407

385

181

31 .00 210

385

11 1,2

7°

.91 209

F 3 2 11

1,2

7°

P-2

89 209.

1 88 209.

132

1775

955

765

28 5

403

193

2595

5295

4035

1 7885

189/11

GSEducationalVersion GSPublisherVersion 207.0.92.98

.94 209

b4 210.73

210.72

210.91

210.71

210.24

0°

88 ,3 2°

0°

210.71

0°

210.75

210.79

210.93

210.74 5

2 153 9515

570 15

5035

282

570 165

298

155

513

3505

190 230 15

100 11

672

455

21 10 110

124

7°

611

178

52 210.

122 15

1,2

90 205

100

349

110

290 230

110 5

611

300

200 11 30 2

unik Kom6 0.1 m2 5,8

478 5

178

100 230

282 124

300

11 100

344

28 110 158 15

2,5 m2

80 Pom. sanitarne 205 0.25

70 Magazyn zasobów 205 0.24

90 205

11 100

90 205

2895

Pom na odpady 0.29 10,7 m2

90 205

478

acja

110

549

490

242

4355

P-1

296 5 96

2005

Obsługa przyłączy 0.31 12,7 m2

100

90 210

ka za ien Łaz cownic pra 8 0.2 m2 3,6

90 205

180

110 205

172

110 210

100

lnia

90 205

lnia dzie Roz erska keln 90 0.18 2 m 05 4,2 268

60 Winda samochodowa 0.30 19,1 m2

349

90 35

3225

60 ebiera Prz 7 0.2 m2 4,3

110 210 69

3325

34 15

100 288

Kuchnia 0.21 23,9 m2

90 205

ln ywa Zm 9 0.1 2 6m

100

110 205

1295

120 585 Sala restauracji 0.8 92,6 m2

141 110

1345

Pom. sanitarne 0.15 5,7 m2

90 205

100 4785

90 205

P+S

Mag. prod. suchych 16 0.20 3,3 m2

3625 2675

Komunikacja 0.17 14,3 m2 100 1175

niepełn. 0.14 7,4 m2 60

Pom. socjalne 0.26 8,6 m2

90 205

100

123/ 28dla WC

16 15 52 100

Przedsionek 0.9 12,8 m2

90 205

100

WC damskie 0.13 9,5 m2

41 62

108

90 205

11 15 6 80

166

90 205

90 205 33 100

100

Chłodnia 0.23 5,3 m2

196

43 Przedsionek WC 0.12 5 m2

171

Biuro restauracji 0.22 8,5 m2

lacyjny

132 15

379

sionek

132 15

3435

182

262

200 5

38 210.

21 10 15 5

180

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611

87 14

41 210.

611

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495

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495

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410

534

34 210.

28 210.

43 210.

A.3

51 210.

Rzut parteru

.04 210 58 210. BUDYNEK BIUROWY Z FUNKCJĄ USŁUGOWĄ

.14 210

praca dyplomowa I stopnia opracowana w Instytucie Architektury i Urbanistyki OPRACOWAŁ

Aleksander Mastalski, numer dyplomu: 203644

PROMOTOR

dr hab. inż. Anetta Kępczyńska-Walczak, prof. PŁ

SKALA 123/27 1:100

133

0°

88 ,3 2°

0°

,4 7

Story 1.-3. 1:200

91 ,6 8°

332

Pion insta

WC męskie 1.12 9,2 m2

15 15

91 ,6 8°

Przestrzeń biurowa 1.2 184,5 m2

Komunikacja 1.1 10,8 m2

434

100

90 205 210

2825

100

P-1

445

245

100 228

90 205

1275

27 x 17 x 25,9

150

Pom. sanit. 1.7 7,2 m2

835

Archwium/ magazyn 1.4 7,2 m2

308

1675

Pion instalacyjny

345

71 prze WC

90 1.11 205 5 m

90 205

Kuchnia, pom. socjalne 1.3 42,2 m2

282 282

202

Serwerownia 1.5 4,8 m2

540

Repeatable floor plan consists of open space area, two conference rooms, sanitary facility, break room with kitchen, storage room and server room.

242

835

5035

332

334

88 ,3 2°

P-2

Pion obsługujący wentylację mechaniczną

10 11 12 13

2945

510

14 15 16 17 18

8°

655

1345

2075

Przegroda o odporności ogniowej minimum E I 30

131

21 22 23 24

8°

1195 855

Przegroda o parametrach. termicznych i ogniowych właściwych dla przegrody zewnętrznej

328

385

46 113 5

144 5

1,2 7

248

F 3 2 11

1,2

7°

P-2

134

GSEducationalVersion GSPublisherVersion 207.0.92.98

5 0°

88 ,3 2°

0°

9515

0°

555

9015

580

0°

555

580

3435

182

5035

alacyjny

503

228

100

WC dla niepełn. 1.8 7 m2

490

5775

135 15 57 100

181

7°

1865

190

191

162

360

1,2

110 210

110

157

P-1

WC damskie 1.10 9,2 m2

92 206

90 205

Sala konferencyjna 1.15 31,6 m2

282

1685

15 11 12 70 100 100 edsionek przedsionek WC 90 1.9 11 205 5 m2 m2

Przedsionek 1.6 8,5 m2

Sala konferencyjna 1.13 31,9 m2

acja unik Kom4 1.1 m2 ,2 7 1

100

90 205

611

140

30 21 10

611

495

534

6 5

A.4

Rzut pięter 1. - 3.

BUDYNEK BIUROWY Z FUNKCJĄ USŁUGOWĄ praca dyplomowa I stopnia opracowana w Instytucie Architektury i Urbanistyki OPRACOWAŁ

Aleksander Mastalski, numer dyplomu: 203644

PROMOTOR

dr hab. inż. Anetta Kępczyńska-Walczak, prof. PŁ

SKALA 1:100

135

Section P-1 1:200

Story 4. 1:200

Green roof not accessible by people 6

P-2 2° 88

0°

2°

7° ,4 89

2°

4. Floor consists of Cafe/Pub area with sanitary complex and green roof area.

0°

4. Floor P1

90 205

Zaplecze baru 4.3 5,8 m2

227

100

227

245

1595

455

15 16

15 38

Green roof area suitable for planting

107

264

Pion instalacyjny

100

152

90 205

210

145 150

150

90 4,6 m 205

Magazyn baru 4.4 2

90 205

Przedsionek 4.6 6,7 m2

1.-3. Floor for office space

105

90 27 205

90 205

100

Green roof accessible by people

P+S

Łazinka pracownicza 4.5 4,3 m2

WC damskie 4.11 8,5 m2

525 90 205

90 205

135 15 84 100

90 205 26 100

15 159 30

305

142

282 WC dla niepełn. 4.7 7,4 m2

162

1685

WC męskie 4.9 9,3 m2

142

Przedsionek WC 4.10 3,9 m2

3315

Przedsionek WC 4.8 3,9 m2

small trees, accessible by people

Komunikacja 4.1 10,8 m2 Sala baru 4.2 66,2 m2

SZ2

264 1 039

Ground floor

Przegroda o odporności ogniowej minimum E I 30

264

8 3-level automated car parking 264

SF2

136

D2 +23,50

124

+23,00

D-1 Detal

336

+19,69 +19,20

+18,40

435

+18,40

4165

322

+17,04

+15,16

425

+13,80

4175

+12,44

+10,56

425

+9,20

4175

+7,84

+5,96

D1 +4,60

425

1835

+4,60

4175

+1,36

SW1

Klapa ukrywająca techniczną część klatki schodowej prowadzącej do przestrzeni garażu automatycznego

±0,00

995

325

2865

+3,24

200

-1,20

12 200 200

-6,18

PG -7,90

7475

-3,26

-8,78

-8,88

A.7

137Przekró

BUDYNEK BIUROWY Z FUNK

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139

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The future of urban mobility is facing tre-

AUDI Adaptive City Car DIA 2019 Prof.: Sina Mostafavi Prof.: Manuel Kretzer

mendous challenges such as increasing traffic, air pollution, and a lack of car-free zones. In efforts to manage urban planning, cities are considering vehicle bans. The car-sharing alternatives provided by the industry are missing a important factors in comparison to owning a private vehicle, most of all a lack of personal identification. The result of the project is a one-to-one model of an autonomous shared car, that would provide better air quality, room for various activities during driving and the feeling of occupying a personal space a highly individualised experience due to the integration of adaptive technologies and the implementation of living systems and organisms. T

Adaptive lighting systems

Individual door, opens on respecitve sides

Adaptive growing systems

DIA (Saeed Abdwin, Niloufar Rahimi, Aleksander Mastalski, Neady Oduor, Marina Osmolovska, Ashish Varshith) DDD (Jan Boetker, Otto Glockner, Nate Henrdon, Dominique Lohaus, Marie Isabell Pietsch, Katjia Rasbasch, Fu Yi Ser, Lam Sa kiu, Anian Till Stoib, Laura Woodrow) TUTORS: DIA (Adib Khaeez, Valmir Kastrati, Shazwan Mazlan) DDD (Manuel Lukas) DIA (Dessau Institute of Architecture) DDD( Dessau Department of Design)

140

Design Strategies The organic design of the car is based on generative principles that aim at offering an alternative to the standardized appearance of present day concepts. The asymmetric shape of the vehicle allows for more internal space. In addition to a structure that seems naturally grown, certain areas of the shell are designed to encourage the growth of moss and lichen to filter air pollutants entering the cars interior. Integration of large windsheilds, made from opacity-changing smart glass, that direct the visual focus of passengers from unpleasant traffic situations to the environment.

Regular mini two-seater city car

Seating positions rearranged for enhanced human interaction

Cabin interiors and integrated automotive systems

Seamless and wide viewing windows

141

AUDI Adaptive City Car Adaptive Interactive System

Multiple "if case" scenarios were simulated with the adaptive technology and sensors involved in the car to make it smart and react with gestures on interaction with it. Be it a human, an animal or another vehicle, the technology allows to possibly make the care more humane as possible.

Increased intensity with proximity

Object / Person approaches car

Gradually switches lighting color, if the object gets within 30 cms of the car

Color represents different adaptive lighting states of the Car 1 object approaching the car > 1 object approaching the car

2. Strobing, non threatening light

Engine turned on

3. Breathing, friendly light

Engine turned off

4. Strobing, Alarmed light

Person Vehicle Recognized person Unrecognized person Person along with vehicle Moving Vehicle Slow or still vehicle

142

1. Breathing, Welcome light

The sensors respond to the presence of individuals control integrated light and audio feedback in order to increase the passengers identification with the car.

Adaptive Interactive System

Increased intensity with proximity

Object / Person approaches car

Gradually switches lighting color, if the object gets within 30 cms of the car

Color represents different adaptive lighting states of the Car 1 object approaching the car > 1 object approaching the car Person

1. Breathing, Welcome light 2. Strobing, non threatening light 3. Breathing, friendly light

Vehicle Recognized person Unrecognized person

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8 The 1 : 1 model was robotically produced from Expanded Polystyrene in the place of flax fibers, mycelium and wood. The robotic lab provided apt flexibility in achieving the complex geometry.

AUDI Adaptive City Car Fabrication Techniques

Representative picture on breaking down a cluster

The robot had 6 primary axis and 1 secondary axis in the form of a rotatary base, this helped in reaching difficult corners Complex pieces were subdivided into multiple smaller pieces to simplify the fabrication. Each EPS block optimised to produce the least waste, with the sustainability as the focal point in the approach. The blocks that were ready for fabrication had to go through multiple steps as shown.

Phase 1 : Positioning EPS

Hot Wire Cutting

HWC completed

Phase 2 : Milling optimisation

Milling

Piece ready for coating

The first phase is hot wire cutting, to carve the block to add details. With a tool change to milling head in the second phase, details such as the door seam, adaptive elements and porosity were added. The final fabricated element was hand-coated with up to three layers of polystyrene adhesive, sanded and then glued to its neighbours.

144

Assembly Once all units were in place the completed vehicle was painted and the adaptive technologies and organic materials were installed. The total working time on the project exceed 10.000 hours of labor. The 1 : 1 model was built with 104 bespoke components that were assembled on a fourwheel platform sponsored by AUDI.

Glass cluster (4 pieces) Red cluster (36 pieces) Yellow cluster (4 pieces) Blue Cluster (47 pieces) Green Cluster (13 pieces)

Adaptive living systems, cork and moss pieces

Seating arrangement

Adaptive smart glass

Adaptive lighting systems

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HINTERLAND STUDIO Part 1: Research Prof.: Jonas Tratz DIA Winter 2018

Siteplan: Hermsdorf 1:2500

The aim of the studio was to try and find solutions to Berlin’s problem with lack of thestreet housing boundaries of plot boundary spaces. which are not used Common occurrence in this city is to split sites with existing house in half and build another one creating city behind city. This project aims to find and create way to convert this trend into good architecture.

b street boundary

Drawing of

a exisitng plot

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GSPublisherVersion 0.0.100.100

XYZ types 1:1000

XYZ FAMILY Base for this typology XYZ exisitng triangular FAMILY is Base for this typology is plot, or several plots exisitng triangular plot, or with possible addition several plots with possible of dimetric shape. The addition of dimetric shape. The conjoinedplots plots form conjoined form interesting hatchet, interesting hatchet, or or hammer like shape. If the hammer like shape. If the conglomeration doesn't conglomeration have connection doesn’t to the have connection to the the street the shape is just: "head" the without the handle. street shape is just: the “head” without the boundaries of plot handle. which are not used

Drawing of

created plot

b boundaries of plot which are not used

street boundary

XYZ types 1:1000

STUDIO HINTERLAND FAKT / Jonas Tratz

Aleksander Mastalski

Siteplan: Berlin, Hermsdorf

Siteplan: Hermsdorf 1:2500

XYZ

FAMILY

Base for this typology is

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8 61 3485 8

4636 8

III

9 I

ST PLOT c, 1:500

HINTERLAND STUDIO Part 2: Strategies Prof.: Jonas Tratz DIA Winter 2018 4711 8

2890 8

4634 8

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1282

1281

7039 8

20% plot - 95m2

STRATEGY 1

STRATEGY 1 Second stage of the studio was to develop three different strategies which could be applied to every found site.

1st strategy is about privacy and creation of individual space, building is inside hinterland so it has to create its own independent and exclusive area. It is achieved by reversing the classical idea of garden surrounding building and thus creating structure similar to ancient Rome atrium houses. Depending on the size of the plot the house has one or two storeys. #1 Building's external walls are put as close to plot boundaries as possible, mimicking them; #2 Inside of the builiding is cut away and secluded garden is put in its place #3a For smaller plots the first floor is placed in a way the sunlight can reach garden and rooms on ground floor. The 'living' space is on ground floor, while bedrooms and tarrace on second. (PLOTS b,c)

ground floor

#3b For bigger plots the first floor is the only floor, living space is put on one wing and bedrooms on the other (PLOT a)

living space

1st floor

bedrooms

Sun rays

Strategy Explanation Drawing

150

2nd fam scul thus bas belo divi and

ace, pace, own own ing ersing hus d thus ses. ouses. twotwo or

STRATEGY 2

STRATEGY 3

STRATEGY STRATEGY2 2

STRATEGY STRATEGY3 3

2nd strategy is is about one bigger house that can fit fit single 2nd strategy about one bigger house that can single family, butbutthethebuilding family, buildingis israther ratherto tobebeperceived perceivedas asa a sculpture. Every plot in in this family is is interesting onon its its own, sculpture. Every plot this family interesting own, thus needing more individual approach. Shape design is is thus needing more individual approach. Shape design based based onon their their own own boundaries(precisely boundaries(precisely explained explained below). ByBy combining thethe triangles together, thethe space is is below). combining triangles together, space divided and produces interesting game between building divided and produces interesting game between building and its its surroundings. and surroundings.

3rd3rd strategy is is about creating complex of of 2-32-3 houses that strategy about creating complex houses that connect to to each other viavia common center garden, which connect each other common center garden, which becomes semi -public space. This concept is is suitable forfor becomes semi -public space. This concept suitable social-housing. social-housing.

ries daries

n is is den 2

first e first ing living ing wing her other

2 3

#1 Divide plot in shape as as close #1 Divide plot in shape close to the right triangles as as to the right triangles possible possible

#1 Houses areare scatterd onon opposite parts of of plot's verticies #1 Houses scatterd opposite parts plot's verticies #2 #2 Floor plan shape corresponds to to adjacent boundaries of of Floor plan shape corresponds adjacent boundaries thethe plot and simple geometric rules presented below plot and simple geometric rules presented below #3 #3 Despite not being connected directly to each other the Despite not being connected directly to each other the buildings areare opened to to thethe common space. buildings opened common space. #4 #4 In In order to to maintain percentage of of plot that is green thethe order maintain percentage plot that is green houses are 2 or 3 storeys. houses are 2 or 3 storeys.

Schematic drawing Schematic drawing b b 2 2

Geometric analysis Geometric analysis

a a

#2 #2 Convert 2 triangles to the Convert 2 triangles to the right ones, by by changing thethe right ones, changing bigger angle to 90 degrees bigger angle to 90 degrees 1

ground floor ground floor b

#3 #3 After rescaling, joinjoin thethe twotwo After rescaling, triangles together to create triangles together to create boundaries of the house boundaries of the house

1st 1st floor on on toptop floor of ground floor of ground floor

Strategy Explanation Drawing Strategy Explanation Drawing

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9 744

194 103

743

194 103

I II

TEST PLOT A

7327 194

Ground floor plan

7325 194

II 194 229

STRATEGY 1

7325 II 194

I 744

HINTERLAND STUDIO Part 2: Typological analysis Prof.: Jonas Tratz II DIA Winter 2018 I

7327 194

743

194 229

Ground floor plan

7323 194

II I

7323 194

II 5882 194

7327 194

III II I

II 5882 194

7327 194

III

I 8 58

1047

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II 8 58 7402 194

7739 194

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1047 5833 194

7332 194

7335 194

7334

1046

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194 7333 194

7402

7739 194

7401 194 194

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I I

1046

I II

7335 194

7334 194

7740 194

7401 194

I I

I TEST PLOT a, 1:500

TEST PLOT a, 1:500

152

I II I

20% plot - 211,8m2

Ground floor plan

STRATEGY 2

STRATEGY 3

Ground floor plan Ground floor plan

1st floor plan

floor plan 1st 1st floor plan

Ground floor plan

floor plan 1st 1st floor plan

Ground floor plan

153

7739 194

7333 194 7332 194

7402 194

7739 194

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7402 194

1046

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1046 7334 194

7335 194

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7334 194

7740 194

HINTERLAND STUDIO I I Part 2: Typological analysisI I I Prof.: Jonas Tratz II I DIA Winter 2018

7401 194

TEST PLOT a, 1:500

STRATEGY 1

20% plot - 211,8m2

TEST PLOT a, 1:500

20% plot - 211,8m2

TEST PLOT B

Ground floor plan

Ground floor plan I 4947 8

4947 8

4948 8

3489 8

3490 8

4948 8

II I

3489 8

3720 8

3590 8

8 51

3590 8

8 58

II 3486 8

8 53

II 8 59

8 61

8 57

I I

4636 8

8 61

3485 8

4711 8

2890 8

4636 8

4634 8

4710 8

2890 8

II 4635 8

1282

III

4711 1281 8 7039 8

4634 8

4710 8

4635 8

1282

III

154 TEST PLOT b, 1:500 7039 8

1st floor plan

8 54

8 56

8 60

3485 8I

8 56

8 60

8 57

8 55

8 58

8 52

8 54

8 55

II 8 59

8 51

8 50

8 52

8 53

3722 8

3723 8

3725 8

II 3486 8

8 50

6136 8

3725 8

3722 8

3589 3723 8 8

3720 8 3486 8

6136 8

3490 8

3589 8

1281

20% plot - 118,5m2

1st floor plan

STRATEGY 2

Ground Ground floor floor plan plan

STRATEGY 3

Ground Ground floor floor plan plan

2nd floor 2nd floor plan plan

1st floor plan plan 1st floor

3rd floor 3rd floor plan plan

155

3485 8

8 614636 8

3485 8

4711 8

II 2890 8

III

4635 8

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HINTERLAND STUDIO Part 2: Typological analysis Prof.: Jonas Tratz DIA Winter 2018

1282

7039 8

4636 8

4634 8

4710 8

4634 8

4710 8

4635 8

1282

1281

EST PLOT b, 1:500

STRATEGY 1

20% plot - 118,5m2

7039 8

TEST PLOT b, 1:500

20% plot - 118,5m2

TEST PLOT B

Ground floor plan

I 4947 8

4948 8

4947 8

II 7406 8

3489 8

3490 8

3489 8

3589 8

3486 8

3725 3490 8

8 51

6136 8 W

8 53

3590 8

3589 8

3725 8

8 55

8 58

3486 8

3590 8

3722 8

3723

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7405 8

7406 8I

4948 8

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6136 8

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3723 II8

8 53

8 59

II II

3486 8

1st floor plan

8 60

8 51

8 58

8 61

8 55

II I

II 8 59

3485 II8

1st floor plan

8 4636 60 8

II 3485

2890 8

4711 8

4634 8

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1282

III

4636 8

1281 7039 8

4711 8

I 2890 8

8 61

4710 8

4634 8

1282

III

EST PLOT c, 1:500 156

1281

7039 8

20% plot - 95m2

I I

STRATEGY 1

3rd floor plan

STRATEGY 2

STRATEGY 3

Ground floor plan

1st floor plan

STRATEGY 2

STRATEGY 3

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Final stage of the studio was to choose one plot and one strategy and convert them into detailed house design

Ground floor plan 1:200 1:100

420

360

500 418

100 280 180 12

267

13 18

229

10,2

0.9

Eating

11,7

0.10

Living room

29,9

0.2

4 180

90 205

180 156

100 205

396

217

396

1 258

600 80

109

3 -0,15

0.10

270

0 23 1 46

246

322

270

0.9

246

322

0.8

428

230 461

396 280

s0 53

10%

12,89%

158

0.6

90 205

0.7

120

90 205

W12

109

420

353

0.3 12

90 280

29 16

396

360

449

420

360

230 690

560

Southern facade

1 550

1:100

360

1 470

28 301

158

360

180 280

921 29

330

306

s0 s0

28 24

28 330

250 52 50

164

0.5

303

Kitchen

240

2,1

0.8

89,7 m²

420

Laundry, Mech. Ventilation

0.1

12 19 63 90 205

90 205

261

180

306

2,8

0.7

1 256

5,5

1 200

Bathroom

0.6

237

13,3

0.5

532

50 280

0.4

215

8,7

Bedroom

183

250 280

Studio

0.4

112

0.2

±0,00

-0,05

5,5

450

150

Hall

303

870

476

154

919

301

0.1

Ground Floor S2

382

HINTERLAND STUDIO Part 3: Detailed house design Prof.: Jonas Tratz DIA Winter 2018

Site Plan

159

690

396 280 360

396

360

420

1 470

301

1 570

1 470

1 550

Northern facade

1:100

420

382

360

246

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246

270 28

270

Southern facade

1:100

+5,39

+4,61

+3,65

+2,80

±0,00

-0,15

Western facade

1:100

-0,15

+5,39

Eastern facade

1:100

+5,39 +4,61

+3,65

+2,80

±0,00

-0,15

Section

±0,00

-0,15

1:100

Section

1:100

+5,39

+4,61 +4,35 +4,16

+4,16 +3,65

+3,65

+2,80

-0,15

-0,05

-1,15

+3,65

+3,45 +3,25

±0,00

160

205

±0,00

Section S1

450

240

+2,80

205

280

+3,25

420

Section S2

360

±0,00

330

180

±0,00

420

270

932 150

Northern facade

1:100

+5,39

450

240

450

Southern facade

1:100

Western facade

1:100 +5,39

+4,61

330

+5,39

532

+2,80

180

+2,80

S2 301

16 24

28 250

0.4

0.3

396

±0,00 24

158

1:100

360

0.9

382

+2,80

360 +3,25

449 690

1 470 1 550

±0,00

Northern facade 450

240

420

-1,15

330

Northern facade

Southern facade

360

205 -1,15

±0,00

W E

-0,05

-0,15

1:100

180

Western facade 4 3

±0,00

1:100 420

270

1:100 +5,39 1

2 +5,39

+3,65

±0,00

-0,15

205

280

±0,00

230

420

301

1 570

s0 +3,65

+4,35

396

+2,80 360

1 470

23 4

+5,39

396 280

+2,80

-1,15

+3,65

+3,25

-0,05

-1,15

0.10 1:100 A

246

322

0.8

-0,15

+3,65

217

396

90 205

±0,00

420

Section C

+3,65

+2,80

+3,65

+3,45 +2,80

+2,80

205

690

Eastern facade

0.6

GSPublisherVersion 0.0.100.99

+3,65

7405 8

353

600 80

303 16

109

90 +3,65 75 205

240

270

+4,61

180 280

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690

180

0.7

120

90 205

90 280

W12

428

2 450

100 205

156

s0 84

Northern facade S2

164

1 470 109 24 1 570

1 256

1 200 420

±0,00

+4,61

420

0.2

52 50

261

180

12 19 63 90 205

90 205 +3,25 420 0.5

+5,39

360

0.1

+2,80

+3,45 280

240

21 24 21

180 1 200

1 270

+4,16

250 280

215

420

+4,16

±0,00

+4,61

E +5,39 +4,61

+3,65

+2,80

-0,15

±0,00

-0,15

1:100

-0,05

1:100 1:100

±0,00

Section

450

237

360

Eastern facade Section

330

330 +4,16

-0,15

150

229

+4,61

267

+3,65 +4,16

100 280 12

532

+2,80

180

532

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183

+4,61

+3,65

306

+5,39

270

+5,39

420

500 418

-0,15

476

112

E S1

322

1:100 2

Western facade

360

306

420

303

28 24

870

154

Western facade

450

919 28

240

-0,15

780

450

Ground Floor

150

±0,00

-0,15

280

638

870

±0,00

1 270

S2 932

-0,15

1:100 1 200

±0,00

+3,65

Foundation

+5,39 +4,61

+3,65

553

+3,65

+4,61

230 461

553

638

870 780

Southern facade ±0,00

-0,15

161

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Professional Work: Single-family house near Lodz Supervision: arch. Iwona Szymczyk-Mastalska

163

10 Ground floor plan 1:200

Nazwa pomieszczenia

Posadzka

Powierzc

0.0

Garaż

gres

52,1

0.1

Wiatrołap

gres

5,2

0.2

Garderoba

parkiet

5,1

0.3

Klatka schodowa

gres

14,0

0.4

Pokój

parkiet

15,0

0.5

Garderoba

parkiet

5,6

0.6

łazienka

gres

9,4

0.7

Spiżarnia

gres

7,8

0.8

Kuchnia

gres

10,5

0.9

Jadalnia

gres

9,6

0.10 Salon

parkiet

27,4

0.11

Parter

Siłownia

gres

15,5

0.12 łazienka

gres

4,2

1.1

Atelier

gres

23,3

1.2

Pokój

parkiet

24,9

1.3

Łazienka

gres

8,9

1.4

Pokój

parkiet

20,6

1.5

gres

4,9

1.6

Pralnia

gres

9,6

181,4 m² Piętro

92,2 m²

273,6 m²

Mikro.

Piek.

1 17

2 18 x 175 x 28

Aranżacja parteru

D.6

PROJEKT DOMU JEDNORODZIN ADRES

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PROJEKTANT GŁÓWNY PROJEKTANT

pow. poddębicki; gm. Dalików; obr. Sarnówek; dz. 145/9, 145/10 IWONA SZYMCZYK-MASTALSKA mgr inż. arch. nr ew. upr. 442/94/WŁ ALEKSANDER MASTALSKI inż. arch.

165

pralka suszarka

POMPA CIEPŁA

KLIMATYZATOR

First floor plan 1:200

STRON

Aranżacja poddasza

D.7

ARCHITE

PROJEKT DOMU JEDNORODZINNEGO

166

ADRES PROJEKTANT GŁÓWNY

pow. poddębicki; gm. Dalików; obr. Sarnówek; dz. 145/9, 145/10 IWONA SZYMCZYK-MASTALSKA mgr inż. arch. nr ew. upr. 442/94/WŁ

PODP

167

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168

169

Aleksander Mastalski mastalski,aleksander@gmail.com +48 513 744 200