TU BERLIN - Artificial Natures Seminar Paper by Omar Fouda

Artificial Natures I Decoding

The Future of Architecture and Urban Design between Computational Thinking and Machine Learning Group 04 A seminar in collaboration between CyPhyLab, Technsiche Universität Berlin and ESU Lab, Mansoura University funded by DAAD 2019-2021

Technische Universität Berlin Faculty VI Planning Building Environment | Institute of Architecture Chair of Bio-inspired Architecture and Sensoric Prof. Liss C. Werner Teaching Assistant: Zvonko Vugreshek

Mansoura University ESU Lab Prof. Sherief Sheta

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Authors: Al-Ashwah, Fatma Fouda, Omar Hegaz, Mohamed 1 Tanta

University, fatma_alashwah@f-eng.tanta.edu.eg Mansoura University, omaribrahim9855@std.mans.edu.eg 3 Technische Universität Berlin, m.hegaz@campus.tu-berlin.de 2

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Table of Contents LIST OF FIGURES .................................................................................................................................. 4 1. INTRODUCTION............................................................................................................................... 7 2. NETWORK DIAGRAM OF MODULES ............................................................................................ 7

3. MODULE I: COMPUTATIONAL THINKING .................................................................................... 8 4. MODULE II: COMPUTATIONAL TOOLS ...................................................................................... 14 5. MODULE III: COMPUTATIONAL URBAN ANALYSIS ................................................................. 23 6. MODULE IV: MACHINE LEARNING ............................................................................................. 30 7. MODULE V: COMPUTATIONAL BIOLOGY ................................................................................. 37

8. CONCLUSION ................................................................................................................................ 46 9. BIBLIOGRAHPY............................................................................................................................. 47

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

List of Figures FIGURE 2.1: OWN DIAGRAM, PRESENTS THE OVERLAPPING BETWEEN ALL DIFFERENT MODULES WORKING TOGETHER AS ONE SYSTEM. ............................................................................................................... 7 FIGURE 3.1: OWN DIAGRAM, PRESENTS THE LIST OF COMPUTATIONAL THINKING ACTIVITIES ACCORDING TO VARIOUS REFERENCES.. ..................................................................................................................... 8 FIGURE 3.2: OWN DIAGRAM, PRESENTS THE LIST OF COMPUTATIONAL THINKING COMPONENTS ACCORDING TO VARIOUS REFERENCES ....................................................................................................................... 8 FIGURE 3.3: SHOWS THE MAIN FOUR COMPONENTS OF COMPUTATIONAL THINKING (MCNICHOLL, 2018, P. 37) 9 FIGURE 3.4: OWN DIAGRAM, SHOWS THE DIFFERENCE BETWEEN DIGITAL DESIGN AND COMPUTATIONAL DESIGN.. .......................................................................................................................................... 10 FIGURE 3.5: 'ZIELENBAU' HOUSING BLOCK PROPOSAL BY WALTER GROPIUS. .............................................. 11 FIGURE 3.6: OWN DIAGRAM, SHOWS THE COMPUTATIONAL DESIGN PROCESS. ............................................ 11 FIGURE 3.7: SHOWS THE NUMBER OF TIMES EACH CD TERM APPEARED IN THE LITERATURE BETWEEN 1978 AND 2018 (CAETANO, SANTOS., & LEITÃO, 2020, P. 291).. ................................................................ 12 FIGURE 3.8: OWN DIAGRAM, SHOWS THE FLOWCHART DIAGRAM OF THE NILE TOWER. ................................ 13 FIGURE 3.9: OWN DIAGRAM, SHOWS THE CONCEPTUAL DIAGRAM OF THE NILE TOWER. ............................... 13 FIGURE 4.1: OWN DIAGRAM, DIFFERENT VALUES OF COMPUTATIONAL TOOLS . ........................................... 15 FIGURE 4.2: OWN DIAGRAM, DIFFERENT VALUES OF COMPUTATIONAL TOOLS THROUGHOUT THE PROCESS.. 15 FIGURE 4.3: GU, N., & AMINIBEHBAHANI, P.(2021), A CIRITICAL REVIEW OF COPUTATIONAL CREATIVITY IN BUILT ENVRIONMENT DESIGN. MDPI BUILDINGS, TABLE SHOWING THE THREE LEVELS OF COMPUTATIONAL CREATIVITY . .......................................................................................................... 16 FIGURE 4.4: GU, N., & AMINIBEHBAHANI, P.(2021), A CIRITICAL REVIEW OF COPUTATIONAL CREATIVITY IN BUILT ENVRIONMENT DESIGN. MDPI BUILDINGS, DIAGRAM SHOWING THE RELATIONSHIP BETWEEN HUMANS AND COMPUTERS . .............................................................................................................. 16 FIGURE 4.5: OWN DIAGRAM, DIAGRAM SHOWS HOW COMPUTATIONAL TOOLS EMPHASIZES MAIN CONNECTION BETWEEN DESIGN, URBAN, SOCIAL AND TECHNOLOGY . .................................................................... 17 FIGURE 4.6: OMAR FOUDA PREVIOUS WORK, IMAGE OF A KINETIC SHADING UNIT FACADE . .......................... 18 FIGURE 4.7: OMAR FOUDA PREVIOUS WORK, IMAGES SHOWING HOW COMPUTATIONAL TOOLS CAN ADD INFORMATION TO DIFFERENT MORPHOLOGIES . .................................................................................. 18 FIGURE 4.8: OWN DIAGRAM, DIAGRAM SHOWING THE NON-COMPUTATIONAL DESIGN PROCESS . .................. 19 FIGURE 4.9: OWN DIAGRAM, DIAGRAM SHOW DESIGN PROCESS CAN DEVELOP THE DESIGN PROCESS STEPS ....................................................................................................................................................... 19 FIGURE 4.10: OMAR FOUDA PREVIOUS WORK,IMAGE SHOWS MATERIAL PHYSICS INVESTIGATION THROUGH FORM FINDING TECHNIQUES . ............................................................................................................ 19 FIGURE 4.11: OMAR FOUDA PREVIOUS WORK, VIDEO SHOWING THE REAL TIME EXPLORATION OF MATERIAL PROPERTIES AND CHARACTERISTICS . ............................................................................................... 19 FIGURE 4.12: GOOGLE EARTH IMAGE, MANSOURA CASE STUDY 3 SITES LOCATION . ................................ 20 FIGURE 4.13: OWN DIAGRAM, DIAGRAM ANALYZING OUR SITE MAIN FUNCTIONS AND DECOMPOSING THE PRIORITIES FOR OUR FUNCTIONS....................................................................................................... 20 FIGURE 4.14: OWN DIAGRAM, DIAGRAM SHOWING WORK SYSTEM WHICH IS DEFINED BY SET OF RULES IN ORDER TO EVALUATE THE FINAL OUTCOME ........................................................................................ 21 FIGURE 4.15: OWN DIAGRAM, IMAGES SHOWING THE SITE 3D MODEL . ...................................................... 21 FIGURE 4.16: OWN DIAGRAM, IMAGES SHOWING EXISTED TREES IN THE SITE . ........................................... 21 FIGURE 4.17: OWN DIAGRAM, IMAGES SHOWING THE SOLAR RADIATION STEPS .......................................... 21 FIGURE 4.18: OWN DIAGRAM, DIAGRAM SHOWING THE DEFINITION AND STEPS OF WORKING . ..................... 22

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

FIGURE 4.19: OWN DIAGRAM, IMAGE SHOWING FAST ARCHITECTURE PROTOTYPING AND ITERATIONS . ....... 22 FIGURE 4.20: OWN DIAGRAM, DIAGRAM SHOWS ARCHITECTURE PHASES WITHOUT COMPUTATIONAL . ......... 22 FIGURE 4.21: OWN DIAGRAM, DIAGRAM SHOWING ARCHITECTURE PHASES WITH COMPUTATIONAL ............. 22 FIGURE 5.1: OWN DIAGRAM, SHOWS STEPS OF DEALING WITH DIFFERENT DATA . ....................................... 23 FIGURE 5.2: OWN DIAGRAM, SHOWS METHODS TO DEAL WITH DIFFERENT SOURCES OF CREATIVITY . ........... 23 FIGURE 5.3: OWN DIAGRAM, INTRODUCING ALGORITHMS WORKFLOW . ....................................................... 23 FIGURE 5.4: OWN DIAGRAM, SHOWING THE DEEPER UNDERSTANDING OF OUR CONTEXT THROUGH DATA SETS . ...................................................................................................................................................... 24 FIGURE 5.5: OWN DIAGRAM BUILT WITH MAPBOX,IMAGES SHOW EXAMPLE ON OUR MANSOURA CASE STUDY SITE ON DATA SETS LAYERING USING AVAILABLE API DATA SETS . ....................................................... 24 FIGURE 5.6: OWN DIAGRAM BUILT WITH MAPBOX, IMAGES SHOWING THE ABILITY TO EXPLORE INTERRELATED MAPPING DYNAMICS IN A STATIC MANNER . ......................................................................................... 24 FIGURE 5.7: OWN DIAGRAM BUILT WITH MAPBOX, IMAGES SHOWING THE ABILITY TO EXPLORE INTERRELATED MAPPING DYNAMICS IN A DYNAMIC APPROACH . .................................................................................. 25 FIGURE 5.8: OWN DIAGRAM, DIAGRAM SHOWS THE FIRST APPROACH OF STUDYING THE CURRENT SITUATION OF THE SITE . ................................................................................................................................... 25 FIGURE 5.9: GOOGLE EARTH IMAGE, MANSOURA CASE STUDY 3 SITES LOCATION . ................................... 26 FIGURE 5.10: OWN DIAGRAM, IMAGE SHOWS STREET NETWORK OF THE CASE STUDY SITE . ...................... 26 FIGURE 5.11: OWN DIAGRAM, IMAGES SHOW SURROUNDINGS, CONSTRAINTS AND FULL SITE MAPS . .......... 26 FIGURE 5.12: OWN DIAGRAM, DIAGRAM SHOWS SYSTEM AND SET OF RULES ON WHICH THIS ANALYSIS WAS BASED ON . .................................................................................................................................... 27 FIGURE 5.13: OWN DIAGRAM, IMAGES SHOWING THE VISUAL ANALYSIS, HIGH VALUE POINTS RECORDS AND THE FINAL POSITION OF THE POINTS WHICH ARE THE MAIN ATTRACTION POINTS . .................................. 27 FIGURE 5.14: OWN DIAGRAM, DIAGRAM SHOWING THE MODULAR SEQUENCE IN WHICH THE ALGORITHM WAS BUILT AND BASED ON . ...................................................................................................................... 28 FIGURE 5.15: OWN DIAGRAM, IMAGES & VIDEO SHOWING DIFFERENT VARIATIONS AND ITERATIONS BASED ON THE FLOOR AREA RATIO FOR EACH FUNCTION .................................................................................... 28 FIGURE 5.16: BUILT WITH BLUESHIFT, DATA FROM WORLD POP, IMAGE SHOWING ONE YEAR OF AIR PASSENGER TRAFFIC . ......................................................................................................................................... 29 FIGURE 5.17: CITY SCIENCE LAB, HAFENCITY UNIVERSITY, IMAGE SHOWING DATA OF HAMBURG CITY SHOWN ON A PROGRAMMED PLATFORM . ....................................................................................................... 29 FIGURE 5.18: OWN DIAGRAM BUILT WITH CARTO, IMAGE SHOWING THE TRANSFER OF THE WORLD MAP FROM A STATIC MAP TO A DYNAMIC MAP, THIS DYNAMIC SET OF DATA IS FOR GLOBAL COVID-19 CONFIRMED CASES, SHOWING THEIR LOCATION AND TRAILS ALL OVER THE WORLD . ............................................... 29 FIGURE 6.1: ML IMPORTANT BRANCHES (SOMVANSHI, CHAVAN, TAMBADE, & SHINDE, 2016) . .................... 30 FIGURE 6.2: TRAIN AN ALGORITHM TO PERFORM CLASSIFICATION AND REGRESSION IN SUPERVISED LEARNING (AYOUB, GRISIUTE, PEREZ, & YE, WS 2018/2019) . ......................................................... 31 FIGURE 6.3: ML OPERATING SCHEME (GARCÍA, MORENO-LEÓN, ROMÁN-GONZÁLEZ, & ROBLES, 2020). .... 31 FIGURE 6.4: TRAIN AN ALGORITHM TO PERFORM CLUSTERS AND ASSOCIATIONS IN UN-SUPERVISED LEARNING (AYOUB, GRISIUTE, PEREZ, & YE, WS 2018/2019) . ...................................................... 32 FIGURE 6.5: THE CONCEPT OF NEURAL STYLE TRANSFER, (SOURCE: AI_ STYLE TRANSFER _BUILDINGS _ MICHAEL HASEY, HTTP://WWW.MICHAELHASEY.COM/AI_STYLETRANSFER_BUILDINGS, ACCESSED, (206-2021). ......................................................................................................................................... 33 FIGURE 6.6: THE NEURAL STYLE TRANSFER RESULT, (SOURCE: AI_STYLETRANSFER_BUILDINGS — MICHAEL HASEY, HTTP://WWW.MICHAELHASEY.COM/AI_STYLETRANSFER_BUILDINGS, ACCESSED,(20-6-2021) . ....................................................................................................................................................... 33

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

FIGURE 6.7: CLASSIFICATION OF THE BUILDING TYPOLOGIES USING A SUPERVISED MACHINE LEARNING METHOD CALLED IMAGE CLASSIFICATION (HUBER & GHASHGHAEI, WS 2018/2019) . ........................ 34 FIGURE 6.8: WORK FLOW DIAGRAM OF ML TO PREDICT NEW CITIES MORPHOLOGIES OUT OF GLOBAL NORTH AND GLOBAL SOUTH CITIES (EKA, MOHAMED, & YASIN, 2020) ............................................................ 35 FIGURE 6.9: SAMPLE OF THE GLOBAL NORTH CITIES WHICH HAS BEEN FEED TO THE MACHINE, STARA ZAGORA, BULGARIA. ....................................................................................................................................... 36 FIGURE 6.10: SAMPLE OF THE GLOBAL SOUTH CITIES WHICH HAS BEEN FEED TO THE MACHINE, CASABLANCA, MOROCCO. ...................................................................................................................................... 36 FIGURE 6.11: SAMPLE OF THE TRAINED MODEL OUTCOME IN THE 3RD STAGE WHICH REPRESENT THE COMBINATION BETWEEN GLOBAL NORTH AND GLOBAL SOUTH CITIES. .................................................. 36 FIGURE 7.1: SHOWS THE SIMULATION OF HUMAN BRAIN’S NEURAL NETWORK IN ARTIFICIAL COMPUTING SYSTEMS (YANG, ET AL., 2020). ....................................................................................................... 38 FIGURE 7.2: THE CONCEPT OF A BIOLOGY COMPUTING SYSTEM (DOGARU, 2008), P.2).............................. 39 FIGURE 7.3: THE BASIC MORPHOGENETIC EFFECTORS (RAMIREZ-FIGUEROA, DADE-ROBERTSON, & HERNAN, 2013),P.53) .................................................................................................................................... 40 FIGURE 7.4: SYNTHMORPH SHOWS RANDOMLY POSITIONED ATTRACTOR (RAMIREZ-FIGUEROA, DADEROBERTSON, & HERNAN, 2013), P.45) . ........................................................................................... 41 FIGURE 7.5: GEOMETRY DERIVED FROM CELL BEHAVIOR USING SYNTHMORPH (RAMIREZ-FIGUEROA, DADEROBERTSON, & HERNAN, 2013), P.58)............................................................................................. 41 FIGURE 7.6: SHOWS GLOBAL, REGIONAL AND LOCAL MORPHOLOGY.THE PRIMARY BRANCH WHICH CONSOLIDATES INTO THE ‘SHORTEST PATH’ AND MAIN INFRASTRUCTURAL LINK FORMING THE EDGES OF EACH CELL IN THE VORONOI TYPOLOGY (WERNER, 2018), P.6).......................................................... 42 FIGURE 7.7: OWN DIAGRAM, SHOWING THE MAIN PROCESS OF CREATING THE KINETIC SHADING ELEMENT. ... 42 FIGURE 7.8: IMAGES SHOWING THE MIMICKING OF THE FORM AND SHAPE OF THE BUD. ............................... 42 FIGURE 7.9: ILLUSTRATIONS SHOWING THE STRUCTURE MECHANISM OF THE UNIT (OMAR FOUDA PREVIOUS WORK) …………………… ............................................................................................................... 43 FIGURE 7.10: IMAGE SHOWS THE UNIT FULL STRUCTURE ON A FAÇADE (OMAR FOUDA PREVIOUS WORK) …………………… .......................................................................................................................... 43 FIGURE 7.11: IMAGE SHOWS THE UNIT MATERIAL AND HOW MIMICKING CAN GO FURTHER TOWARDS EXPLORING AND ABSTRACTING PROPERTIES (OMAR FOUDA PREVIOUS WORK). ..................................................... 43 FIGURE 7.12: IMAGES SHOW THE BEHAVIOR OF THE MECHANISM SYSTEM TOWARDS MOTION SENSOR (OMAR FOUDA PREVIOUS WORK). ................................................................................................................ 44 FIGURE 7.13: SHOW THE EXPERIMENTAL OF BIOLOGICAL HOUSE. (SOURCE,A LIVING BREATHING BUILDING: HOW BIOLOGY AND ARCHITECTURE WILL CHANGE CONSTRUCTION AND THE BUILT, HTTPS://ARCHINECT.COM/NEWS/ARTICLE/150147904/A-LIVING-BREATHING-BUILDING-HOW-BIOLOGYAND-ARCHITECTURE-WILL-CHANGE-CONSTRUCTION-AND-THE-BUILT-ENVIRONMENT.ACCESSED (20-62021).............................................................................................................................................. 45 FIGURE 7.14: SHOWS THE LIVING HOUSE EXPERIMENT THEMES .SOURCE,A LIVING BREATHING BUILDING: HOW BIOLOGY AND ARCHITECTURE WILL CHANGE CONSTRUCTION AND THE BUILT, HTTPS://ARCHINECT.COM/NEWS/ARTICLE/150147904/A-LIVING-BREATHING-BUILDING-HOW-BIOLOGYAND-ARCHITECTURE-WILL-CHANGE-CONSTRUCTION-AND-THE-BUILT-ENVIRONMENT.ACCESSED(20-62021).............................................................................................................................................. 45 FIGURE 7.15: SHOWS BIRD NESTS (NATURE'S ENGINEERS).(NATURE’S ENGINEERS: BIRD NESTS | THE TRANSIENT BIOLOGIST,HTTPS://THETRANSIENTBIOLOGIST.WORDPRESS.COM/2014/07/09/NATURESENGINEERS-BIRD-NESTS/,ACCESSED 3-7-2021) . .............................................................................. 46

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

1. Introduction With this report we are going to document Artificial nature I seminar, at the beginning of the seminar we had doubts about what we are going to learn, then out of the seminar structure we got knowledge that it consists of five modules, afterwards we got information about Computational thinking, and some related terms, in addition to, machine learning. Within this report we are going to present our understanding of computational thinking, computational tools, urban analysis, machine learning, and computational biology, the relation between all of the them, plus their relation with architecture, and how all of these will affect the future of architecture. Emphasis on some concepts such as computational thinking and that there is no need to use the computer to apply it, computational tools role is not only to speed up the design process. This report represents our starting point in a new field and it also serve a documentation paper for our understanding of the subject through presenting some projects in the different modules to emphasis the essence of each module and how it works. And we are going to work in Mansoura site to apply the essence of computational tools and urban analysis.

2. Network Diagram of Modules Considering all modules as one system, we will find that all modules overlap at some level, the main connection between all of them is the computational tools, where it is the state of mind and thinking to begin with. Any module will have to be related to computational tools as a part of evolving and developing, which is generally generated from computational thinking, therefore, all modules will be centered around computational tools as an action center and computational thinking as a conceptual center. Building from that perspective we can see the connections between other modules in the basic thinking such as, understanding systems, observing complex behavior, etc.…, and also at the tooling and action, such as, variations, evaluation matrix, scripted processes, etc.… And finally, the AI center which is machine learning (third center), through the evolving of technology AI and machine learning became essential to different fields including urban and biology, while also machine learning is based on computational thinking as a conceptual center and computational tools as action center

Figure 2.1: Diagram presents the overlapping between all different modules working together as one system (own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

3. Module I: Computational Thinking Teacher(s): Prof. Liss C. Werner 3.1. What is in essence computational thinking? To get the knowledge about computational thinking essence, it is vital to study it from different approaches as it is a science could be applied in different fields, then narrow down our focus point to get out our own understanding. Computational thinking has various definitions, it depends on which aspect defines it, so there is no specific clear definition for, but most of the definitions have a common term about the process of reformulating the problems and its solutions in a way that can be computed. Simply it could be described as a problem-solving skill set, while architects, consider it as a design approach which focusing on a design process and the result of a computed process. Maximize the role of the digital tools in the design process itself, not only using tools to expedite the design process calculation (Yadav, Good, Voogt, & Fisser, 2017). According to our understanding, Computational Thinking is the process of decoding the problems based on scientific thinking to reformulate the problems and express their solutions in a way that can be effectively executed through breaking the complex problem into a series of simple problems in a way that both humans and computers can understand it. As computational thinking has different definitions and considered as a problem-solving skill, it has different applications, and accordingly it has a list for activities in practical fields but according to various references some of the activities are common e.g. (Automation and algorithmic thinking) while others not (figure 3.1) (Cansu & Cansu, 2019)

Figure 3.1: List of computational thinking activities according to various references as shown, while some of them are common and others not (Own diagram).

For computational thinking application, there is a sequence of components that we have to follow to reach out the final result, according to different references components are various between 4 to 6 (figure 3.2), out of these various components there main four basic components which are considered the main pillars for computational thinking (figure 3.3).

Figure 3.2: List of computational thinking components according to various references as shown. Some of them has common components (Own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 3.3: Shows the main four components of computational thinking (McNicholl, 2018, p. 37).

The punch of the necessary skills to decode complex problems into series of problems that could be tackled by computer without human assistance are the essential abilities of computational thinking. This report will use the description of the main four components (Cansu & Cansu, 2019). • • • •

Decomposition: Is a method of breaking down the complex problem into a series of smaller problems to be more understandable and easier to deal with. Pattern Recognition: Defining similarities and differences within the problems to design the solution pattern. Abstraction: Is the process of making it easier and simpler to understand the problem through decreasing the level of details and variables to move directly for solutions. Algorithms: This is the step of writing the solution step by step so it could be generalized to be used to solve a similar problem later.

After all we can say that it is a way of thinking about how to understand the rules of things and how to get the right equation, in that sense we can reach to the fractions to get the solution. 3.2. How could Architecture/Urban Design Benefit from it? During studying computational thinking term it was clear that there are different definitions and surrounded terms driven from the main term e.g. (computational design, algorithmic design, cybernetics, generative design, etc.) when we dive more into these driven terms it was clear that it has a close relation to architecture and every term of them represent and potential for a design method, backing to the Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

current architecture approaches we will find a design approach called Computational design (CD) which is based on the concept of applying computational strategies to the design process to improve designs and documents the required steps to reach out the final design result, while there is another term called digital design (DD) and it is related to computer tools used in the design (Caetano, Santos., & Leitão, 2020), and in the following figure we can easily define the difference between two different terminologies, computational design and digital design (figure 3.4).

Figure 3.4 shows the difference between Digital design and Computational design (Own diagram).

Back to computational thinking term which in origin has been driven from computed process, we found out that this kind of process and numerical control gave the chance to Marck Burry to complete the unfinished work of Sagrada Família and this is an example of a computational design project with using computer (Burry & Gaudi, 1993) so in that sense we can consider that it gives a new dimension of understanding the geometries and its equations, in addition to, better understanding of the material and how its behavior works, and this open a space for new designs to come up into the life through using the materials differently. Numbers look logic as it has a set of rules on how to deal with, but when it comes for architecture design, we can understand that there is a complexity between the different design layers e.g. (function and form…...etc.) so it crucial to have this set of rules which help to draw the relation between different layers to get more innovative and effective designs. The following example, is a proposal from Walter Gropius and it shows different versions of the design for the same plot because of the rule which had been defined, the main condition was that the daylight should be reached to the lowest point of the buildings and accordingly the distance between the buildings based on buildings height change to full fill the condition. The distance and the number of buildings recalculated, and that provides various iterations of the design so we can compare later to check which one could be the optimum solution based on certain criteria like cost, a number of inhabitants, and percentage of open spaces to the building's footprint, and here we can observe how setting a condition to produce a punch of versions which could be evaluated the get the optimum (figure 3.5).

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 3.5 'Zielenbau' housing block proposal by Walter Gropius

Out of what mentioned above and literature review, we get to know that there is a difference between computational design and conventional design. The following figure shows how the computational design process not only a linear process but it has a kind of decisions loop and evaluation criteria, based on it could proceed further to the final output or back to earlier stages to change inputs or set of rules and this is basically how it helps in the design development process (figure 3.6).

Figure 3.6 show the computational design process (Own diagram).

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Computational design requires gaining knowledge from different fields, this combination between architecture and other fields leads to new design methods which are listed under computational design and there is a difference in popularity of each method (Figure 3.7) (Caetano, Santos., & Leitão, 2020). Parametric Design has a historical evolution. So, it could be defined as the study of the relationship between geometric constraints and dimensions (Caetano, Santos., & Leitão, 2020) Generative Design has the main focus in evolutionary procedures “in both the creation and production processes of design solutions” (Caetano, Santos., & Leitão, 2020)but it could be defined as a design approach that produces multiple and complex solutions based on algorithms (Caetano, Santos., & Leitão, 2020).

Figure 3.7 Shows the number of times each CD term appeared in the literature between 1978 and 2018 (Caetano, Santos., & Leitão, 2020, p. 291).

3.3. Your reflection of benefit of the topic Computational thinking expanding the knowledge and so when we study it we hacking other fields so we get engaged in anthers areas which is mean that it opens-up our minds. helps us to arrange our mind set and how we are going to proceed further for the project, so it helps to design the work flow of the process and the project rules, moving from this point we think about the constraints and requirements to move from the current situation to the admired situation. Case Study: Offices building, Academic project, Mansoura University, 2012 by Mohamed Hegaz (figure 3.8). The project plot is located in Mansoura city, facing the Nile River. Mansoura city has a limited plot for building construction and most of the land is fertile for farming, so it was important to maximize the plot value, in addition, to keep open space for the second row of the buildings, out of the basic information about the plot (current situation empty land) then the target is to maximize the value through having an office building, landscape for surroundings, and keep open space for the second row of the buildings. After defining the problem and the goal start think about the constraints and requirements. Constraints: streets, plot shape, number of floors, footprint percentage, and sun path. Requirements: building function, number of inhabitants, thermal comfort and natural ventilation, building view. After data analysis “current situation, target, constrains, and reequipments” it was the time for work flow diagram to follow the thinking and to manage the mind set to feed the rules “input” into it to get the output (figure 3.8), start working on the design process itself with following of the flowchart diagram (figure 3.9).

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 3.8 Shows the flowchart diagram of the Nile tower (Own diagram)

Figure 3.9 Shows the conceptual diagram of the Nile tower (Own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Observation: After using this way of thinking and flowchart it was quite clearer that it has two sides effects, for the first side it was clear from where to where, there were clear steps to follow to reach out the final product and know in which step we can go back to change the input to get the proper output, even following the same steps with a little change in the inputs produce different versions of outputs which could be evaluated to check the best solution. While on the other side what if there was a wrong step inside the flowchart it was going to lead to a wrong output even though the input was correct, in addition to, it needs a set of criteria for evaluation otherwise there is nothing to guarantee the sustainability of the output. Finally, there was sense that the design was a bit free of imagination it was following a set of rules with answers like yes or no / proceed or back and change but it was not a process of innovation, we are not sure if that positive or negative but we feel that the final output was good, so it seems that this way of thinking will help to produce good designs. 3.4. Your future thoughts for the industry Computational thinking has an effect in architecture, and based on it new design methods has been driven, computational thinking leads to understand the problems till reach the fractions of the problem to get the solution. It will lead to a different manner of dealing with the architecture and design, when we think about the process and the normal order of it, we will find the project moving from design to construction but with better understanding of the materials and its potentials, we will find that the design itself will concern more about the materials, so with this thinking and understanding the characteristics of the materials, new designs will be produced. With computational thinking it will be possible to simplify the complex relations between different zones. It will help to develop the logic of the tools which architects use in the design process to visualize their projects concepts, and based on these developed tools a lot of consumed time will be saved in addition to saving manpower and money which used to hire people, and it will help to save the reparative process to recall it once needed, so this will make a huge impact in the project’s cost.

4. Module II: Computational Tools Teacher(s): Ahmed ElShafie & Sherif Tarabishy 4.1. What are in essence computational tools? 4.1.1 Computational tools value Computational tools are not only about machines that help us produce a final outcome. It is more about managing data in a more insightful manner, specially through the digital turn in the world. As a rule, tools can be characterized as a device that help us do what we know what we must do. We can translate that as the problems that these tools help us to solve are a known problem by us and people involved (Wooley JC, 2005). Therefore, computational tools are not only a digital software that can translate our thoughts in to visuals, it is much more than that, it is a process in which we can think, design and test. So computational tools can be a state of mind, where it starts with data management and organizing in order to be able to transfer these data into a visual presentation, which is then transferred into algorithms, which translate the large amounts of data into meaningful and useful information.

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 4.1: Diagram shows different values of computational tools (own diagram)

Therefore, computational tools have many challenges towards value, appropriateness and usefulness (e.g., aesthetically or functionally (Gu & AminiBehbahani, 2021); which can be described in the following keywords (data Representation, analysis Tools, visualization and databases) (Gaithersburg, 2002). However, computational tools topic is not only about computer aided tools, they are about evolving and transferring our ideas, approaches and goals to a more computational manner, where they can be based on a series of steps based on analyzing certain type of data into forming a system which is defined by roles upon our respond. Computational tools are the main anchor of transferring from object to process, but in an interchangeable manner where the tool is not a tool by its literal meaning, but tool is the way of thinking, managing and organizing data, how you see systems and experience them and act on that manner.

Figure 4.2: Diagram showing different values of computational tools with or without computers (own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Nowadays computational is thought of as a computer aided tool where computers and digital tools are essential for acquiring computational thinking, which is not true, as computational depends on simple steps and logic not about digital visualizing only. This takes us to the importance of discovering the role of digital tools (Computers) and human in the computational process considering each of them as a tool and how they react to each other in the creative process. 4.1.2 Computer – Human role in creativity Creativity is evaluated through the qualities of outputs, the most common qualities are authenticity and appropriateness (Gu & AminiBehbahani, 2021), so when we talk about computational tools, it may conclude several types of agents involved in a process for a certain output. Here we can classify role of computers in creativity to three levels of creativity, computational creativity (CC), human – computer (co-)creativity (HC3) and creativity support tool (CST), these three levels are contextualized in relation to human creativity.

Figure 4.3: The three levels of computational creativity, Invalid source specified.

Here we can see the effect of computational tools as a mandatory element in the evolution of creativity, where at computational creativity level the computational agent is the creator for the output itself (figure 2.3b), and it can also participate with similar computer agents as long as they share the same goal and work on the same output (figure2.3c). While at human-computer (co-)creativity level, the computational agents interact with the human agents during the creative process, either working on the same goal and share creative tasks (figure 2.3c), or at a less powerful agents as generative systems, the computational agents here work with the humans in the creative process by co-sharing creative tasks (figure 2.3d). At a lower level of computational role involvement, where computer does not produce an output on its own, and depends mostly on human creativity, providing him with the tools needed to support human realization and creation (Gu & AminiBehbahani, 2021).

Figure 4.4: The relationship between humans and computers. (Gu & AminiBehbahani, 2021)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

4.2. How could Architecture/Urban Design Benefit from it? 4.2.1 Design – Urban – Social - Technology Computational tools will emphasize the connection between the most important 4 parts of any design process from our perspective (Design – Urban – Social – Technology). As it will start developing the design process from aiming to design a certain object depending on a person’s idea to the final components, to designing the process itself depending on systems which is defined by certain rules and relations in order to generate iterations and variations which can be then evaluated according to our evaluation matrix. Connection between different data sets is an essential role when designing, computational tools offer a new approach towards data management and systems, where urban context data will be available for a direct connection towards the design decisions in order to create a design which can respond to the environment around it. Computational tools will also facilitate the participatory design process through open-source data and real time visualization through keeping history, towards making architecture from people and to people, and most importantly, computational tools will ease and assist research studies towards understanding and exploration.

Figure 4.5: How Computational Tools emphasizes main connection between Design, Urban, Social and Technology (own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

4.2.2 Morphology Computational tools play an important role in morphological studies, through the exploration of materials, behavior and generative systems. It can be used in either preserving and reviving historical structures or creating new morphological structures through the understanding of different systems and relations creating more complexity. Morphology with more detailed information will be transformed from a complex stage to a complexity stage, where it will be available to remodel and re-generate with the same precise measures. Information could be either related to the environment, surroundings, social aspect, urban aspect or technicality.

Figure 4.6: Image of a kinetic shading unit facade (Omar Fouda previous work)

Figure 4.7: Showing how computational tools can add information to different morphologies (Omar Fouda previous work)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

4.2.3 Architecture Dialogue Computational tools enhance the architecture dialogue, starting from the conceptual and research phase all the way to the as-built phase. It will strengthen the connection between different disciplines in the project, helping the architect to coordinate and manage all other stages (interior design, structure, MEP, and etc.…), also will be a great tool in decision making based on different set of data. In a nutshell, computational tools will make a revolution of how to deal with data and how to use them in decision making, designing, constructing and management.

Figure 4.8: Diagram showing the non-computational design process (Own diagram)

Figure 4.9: How design process can develop the design process steps (Own diagram) Figure 4.10: Material physics investigation through form finding techniques (Omar Fouda previous work)

All of this will be possible through computational tools, as it will transfer the design process to a computational design process where the final outcome is interchangeable and can have multiobjective evaluation and different disciplines participation.

Figure 4.11: The real time exploration of material properties and characteristics (Omar Fouda previous work)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

4.3. Your reflection of benefit of the topic We started working on our Mansoura case study site, it consists of three main sites, the first and third sites are public gardens, while the second site, in between, is a culture center building. We started evaluating what is common between the three sites, we found out that the three sites have open area spaces for public, after that we evaluated what are the essential functions needed at an open area space, and each main function needed a set of priorities to be taken in mind, we summed up the priorities to 3 main priorities. Number one is visibility priority, as some functions need to be seen from far places with strong visual axis, number two is more access priority, this also comes with the vision priority as for places with strong visual axis it needs accessibility available as well, and the third one is the thermal comfort priority, as it is very important for functions that need places to rest and sit down. The first two priorities will be analyzed in the urban analysis module, but here we will go through the thermal comfort priority and how to choose places with thermal radiation not higher than a certain limit defined by a rule in a system of simulation which is real time and interchangeable throughout the whole process and evaluation.

Figure 4.12: Mansoura Case Study 3 Sites Location (Google earth image)

Figure 4.13: Analyzing our site main functions and decomposing the priorities for our functions (Own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 4.14: Showing work system which is defined by set of rules in order to evaluate the final outcome (Own diagram)

We started our process by exploring the existing site, studying all surroundings and making a 3d model of our site. Then we located the existing trees in and near our site that could affect us and placed them as well. After that we started creating a solar radiation analysis to discover our radiation ranges on the site, we created a simulation for the whole year, then we created a real time thermal radiation where we can select any time period and start a real time analysis over that time, after that we started creating a rule to manage the site surfaces, where we divide the solar radiation surfaces in to two groups according to radiation limit which is set for maximum allowable radiation (parameter), this gives us two set of surfaces that we can work with, after that we can show the existing surfaces that are suitable for thermal comfort priority functions, and the surfaces that have high radiation can be treated by different ways if needed (greenery – shades – etc.…).

Figure 4.15: Showing the site 3D model (Own diagram)

Figure 4.16: Showing existed trees in the site (Own diagram)

Figure 4.17: Showing the solar radiation steps (Own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 4.18: Definition and steps of working (Own diagram)

4.4. Your future thoughts for the industry 4.4.1 Fast Architecture Computational tools offer a much faster architecture which can be very helpful, specially nowadays during emergencies it will be very handy when it comes to building a huge number of houses and shelters in a very short amount of time yet providing essential needs. Also, iterations provided by computational tools will help a great deal in decision making and placemaking in future to avoid pandemics spread, over Figure 4.19: Showing Fast Architecture prototyping crowdedness and waste of energy and resources, and iterations (Omar Fouda, previous work) while providing a much healthier environment, walkable communities and energy efficient spaces, all of this are possible through defining certain systems. Our vision towards computational tools is related to different levels and phases of architecture industry, where it will be involved throughout project phases starting from the conceptual phase (LOD100) where computational tools will provide a much easier and faster form finding techniques, exploration, prototypes and variations. At the detailed phase (LOD 300) it will offer a new and very efficient technique of material investigation and selection, while at construction phase (LOD 400) it will help in coordinating Figure 4.20: Architecture between different disciplines and Phases without computational collecting data from each one of them (own diagram) dependently and then merging them together, which then improves the safety on-site by discovering early collisions and danger areas to avoid mistakes. In a normal way, each phase deliverables are transferred to the next phase without going Figure 4.21: Architecture Phases back to that phase again, but with computational tools, there will be with computational (own diagram) availability to connect all these phases throughout the project by managing and collecting data in a more ordered way.

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

5. Module III: Computational Urban Analysis Teacher(s): Abdul-Malik Saeed 5.1. What is in essence computational urban analysis? Computational urban analysis presents a new approach of analyzing and evaluating data, where it evolves the conventional design thinking into a computational design thinking by not only aiming for the final outcome, but making the process in which the data/information are analyzed, our main goal. Computational urban analysis describes the importance of how to look beyond the appearance towards the properties, exploring complex behaviors, rules systems and relations. It also describes how to deal with such different potentials in our environments and the source of creativity. There are three methods of dealing with the source of creativity, either by copying to provide certain functions, or abstraction in an innovation way to reproduce what a system does, or by inspiration which reflects the creative ability to create the principles of composition.

Figure 5.2: Shows methods to deal with different sources of creativity (own diagram)

After that, computational urban analysis introduces how to start working with all these different data sets and sources of creativity by presenting algorithms to organize the process in which the data will be used to generate a solution. These algorithms work in a linear order in a very simple manner.

Figure 5.1: Diagram shows steps of dealing with different data (own diagram)

Figure 5.3: Introducing algorithms workflow (own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

5.2. How could Architecture/Urban Design Benefit from it? Computational urban analysis provides a new way of analyzing data, which will affect architecture and urban design towards a more contextualized approach by creating an infrastructure layering of data hierarchy (Road network, centrality, population density and etc.…), this will enhance the decision making and provide data connections which can relate to the environment and how to deal with the existing context.

Figure 5.4: Deeper understanding of our context through data sets (own diagram)

Increased urbanization and connectivity have triggered greater capacities to mine data directly from the sociotechnical systems embedded into urban environments. We are immersed in an entangled ecology of ubiquitous communication infrastructures, from sensors, global positions systems, automated systems to locative media. Which gives us more potentials to discover the latent possibilities of data in urban systems through the application of a set of tools connected to urban analytics.

Figure 5.6: Showing the ability to explore interrelated mapping dynamics in a static manner (own diagram, built with Mapbox)

Figure 5.5: Example on our Mansoura case study site on data sets layering using available API data sets (own diagram, built with Mapbox)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Also discovering the interrelated mapping dynamics to be able to uncover and reveal relationships between urban environments and socio-technical systems. Understanding these types of data, the relation between them, layers comparative analysis and dynamic data sets, are a very essential role for computational urban analysis to improve the capability of managing, organising and comparing data to each other to acquire a research and design approach through a set of mapping strategies.

Figure 5.7: Showing the ability to explore interrelated mapping dynamics in a dynamic approach (own diagram, built with Mapbox)

5.3. Your reflection of benefit of the topic We started to study our Mansoura case study site with two different approaches, the first approach was studying the current situation of the land concerning the main functions and its’ priorities, the other approach was by studying the site future plans by the government, as the site is planned to be on huge project consisting of high-rise building, residential blocks, commercial use and open areas.

Figure 5.8: Diagram shows the first approach of studying the current situation of the site (own diagram)

We started analyzing our case study site in Mansoura into street network, building network and Surroundings, then we started thinking about the main common ground between the three sites, which is open area for entertainment and culture activities.

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 5.9: Mansoura Case Study 3 Sites Location (Google earth image)

Figure 5.10: Street Network of the case study Site (Own diagram)

Then we thought about how to evaluate the most important nodes of connection between the land and people passing by. We based our analysis on the visual impact, where the most points seen from main streets surrounding the site are the high priority points, where it can be an (entrance, gathering point, commercial use, investments area, etc.…).

Figure 5.11: Surroundings, constraints and full site maps (Own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 5.12: System and set of rules on which this analysis was based on (Own diagram)

Figure 5.13: Showing the visual analysis, high value points records and the final position of the points which are the main attraction points (Own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

The second approach is analyzing the future state of the site, as the site is meant to be one mega project (Residential blocks – Commercial use – Green Spaces – Towers). So, we started by selecting our site boundaries and the plots, then we created an algorithm to manipulate floor area ratio for each function which is then extruded by a variety of height limits for each one, and finally creating iterations for each variation to discover the building capability of the site.

Figure 5.14: Modular sequence in which the algorithm was built and based on (Own diagram)

Figure 5.15: Images & video showing different variations and iterations based on the floor area ratio for each function (Own diagram)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

5.4. Your future thoughts for the industry Computational urban analysis is a critical topic in our time and more critical in the future, we think that it will change how the whole world think about urbanism. Changing the world from a static place to a dynamic liveable one with all database networks connected, this will emphasize the role of decision making towards a more ecological approach, where each decision will be based on a deeper analysis and understanding of different layers and set of data. Figure 5.16: Image showing one year of air passenger traffic (Built with Blueshift, data from World Pop)

In the future the whole world will be viewed as a dynamic and static set of data, these set of data will participate in the design process not only at the beginning but also through different stages of the project. This can indicate that IT and programming will play a very important role in the future industry, as everything will be databased. Architecture and urban planning may become only a supervision title in the future. Figure 5.17: Image showing data of Hamburg city shown on a programmed platform (City Science Lab, HafenCity University)

Figure 5.18: Image showing the transfer of the world map from a static map to a dynamic map, this dynamic set of data is for global Covid-19 confirmed cases, showing their location and trails all over the world (Own diagram, built with Carto)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

6. Module IV: Machine Learning Teacher(s): Zvonko Vugreshek 6.1. What are in essence computational tools? Machine Learning (ML) is an important branch of AI that deals with the study and development of algorithms that can learn from data. ML involves training a machine on existing data, which can help us understand the data better and make predictions. This approach requires a training set of data containing examples of past experiences and an algorithm that builds a mathematical model out of the training set samples in order to make predictions or decisions based on a given training set, i.e., to learn without being explicitly programmed to do so. The set of attributes that are associated with an example are called features. For example, to learn how to predict the house prices, we represent houses in terms of features (e.g., total area, number of rooms, coordinates) and then use those features to correlate to their price, i.e., their output feature. ML has three important branches: supervised learning, unsupervised learning, and reinforcement learning (figure 6.1) (García, Moreno-León, Román-González, & Robles, 2020).

Figure 6.1: shows ML Important branches (Somvanshi, Chavan, Tambade, & Shinde, 2016)

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Supervised Learning: the machine uses a data training set containing both the inputs and the corresponding outputs. The outputs are used to guide the learning process. It is used to address, regression, classification, and ranking problems (figure 6.2). For example (predict the species of a flower based on their petal attributes (e.g., length and width). supervised learning work can be summarized by the following (training, learning, evaluating, and exporting) shown in (figure 6.3). (Okhoya, 2014)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 6.2: Train an algorithm to perform classification and regression in supervised learning (Ayoub, Grisiute, Perez, & Ye, WS 2018/2019)

Figure 6.3: shows ML operating scheme (García, Moreno-León, Román-González, & Robles, 2020).

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Unsupervised learning: the machine is provided with data that has no ‘correct answers.’ It is essentially just given the data and asked the machine to try making sense out of it. The goal is to describe how the data is clustered or organized, and, consequently, to make predictions for unforeseen data points (figure 6.4). For example, is to group flowers in different groups according to their petal attributes (e.g., determine the flowers species by grouping flowers according to their petal length)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 6.4: Train an algorithm to perform clusters and associations in unsupervised learning (Ayoub, Grisiute, Perez, Ye WS18/19, 2020)

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Reinforcement Learning: the machine learns how to make better choices. The machine learns by reinforcement that making good choices leads to higher rewards. Concretely, the learning algorithm seeks to make decisions that maximize the reward. (Bel'em, Santos, & Leitão, 2019).

6.2. How could Architecture/Urban Design Benefit from it? ML helps designers in making decisions tasks by providing suggestions for the most appropriate course of action through a recommender system. These systems use the features of each alternative to rank them and generate customized suggestions. This can occur through conversational interfaces, where the system poses questions to capture the architect’s intent, thus making more adequate suggestions. In this case, a knowledge-based must be constructed a priori to maximize the efficiency and range of template suggestions. Recently, a recommender system was explored to create a design-bot that iteratively asks questions with the purpose of enhancing self-communication, and, thus, clarifying the architect's intentions. Both works depend on the knowledge that they were initially programmed with. (Bel'em, Santos, & Leitão, 2019).

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

ML helps designers in emulating the human mental process that articulates non-formal grammars in the development of architectural concepts. By using neural style transfer approaches that use Convolutional Neural Networks (CNNs) to obtain the representation of the style of an input image. This technology used a complex algorithm that allows any image to be re-created in an infinite number of new ways and styles. The work presented in uses two different source images to generate images that mix content and style representation. By preserving the composition of the original image and combining it with the colors and local structures of the image containing the style to transfer, this approach balances content and style (figure 6.5).

Figure6.5: shows the concept of neural style transfer, (Source: AI_styletransfer_buildings — MICHAEL HASEY, http://www.michaelhasey.com/ai_styletransfer_buildings, Accessed, (20-6-2021)

As a result, by using neural style transfer approaches, one can transfer styles of other buildings or other architects, instantiating stylistic variations. Thus, we can achieve mass customization by generating different houses in the same style, provided that we have (1) a representation of the building style we wish to use, (2) a set of base designs to which we ought to apply different style variations, and a (3) previously trained neural style transfer model. To explore style transfer techniques in the context of architecture, we can devise at least two approaches which greatly differ in the representation of designs, hence affecting their implementation. In this approach, the architect provides a set of images that defines a style, as well as the images of the designs to which the style will be applied (figure 6.6) (Zhang & Blasetti, 2020).

Figure6.6: shows the neural style transfer result, (Source: AI_styletransfer_buildings — MICHAEL HASEY, http://www.michaelhasey.com/ai_styletransfer_buildings, Accessed, (20-6-2021)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

ML helps the designers to assess the design’s performance, in order to optimize a design. So that, it is necessary to implement a parametric model of the building. Algorithmic-based approaches are useful to produce that model and the corresponding analytical models used to assess the design’s performance, a key factor in the optimization process. Depending on the optimization goals, different analysis tools perform simulations that evaluate a given analytical model. Hence, an optimization algorithm is responsible for generating different values for the design parameters and, based on the performance evaluations results, guide the search towards more satisfactory designs. (Wortmann & Nannicini, 2016). ML also is useful for anticipating next Intentions and making suggestions. One example is autocompletion of text. ML techniques could predict, based on past coding actions, what fragment of code might be written next. Similarly, the same techniques are applicable to modeling scenarios. In this case, a digital assistant trained on a design corpus containing a large set of well-defined examples, suggests possible model completions. (Bel'em, Santos, & Leitão, 2019). One of the benefits of ML for urban planning is to classify the building typologies using a supervised machine learning method called image classification and create a sorted catalogue. The subsequent step encompasses the application of a template matching algorithm, used to relocate the building typologies on the maps to get more information about their morphological distribution (Figure (6.7). (Huber & Ghashghaei, WS 2018/2019).

Figure 6.7: shows classify the building typologies using a supervised machine learning method called image classification (Huber & Ghashghaei, WS 2018/2019).

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

6.3. Your reflection of benefit of the topic There was industrial revolution in the middle of eighteen’s century and it considered as a turning point in the human history while human labor transformed into using machine (Mohajan, 2019), Back to the history, moving from that point when start depending on the machines and keep developing technology it seems that it’s a normal practice to this moment where we depend on machines in our daily life, moreover we are going to another turning point in our human history, instead of using machines only as tools to finish our work, it’s going to be partner. With the implementation of machine learning in architecture, new designs are going to be invented, as we already mentioned the advantages of machine learning in the previous sections, it’s obvious how architects and students can benefit from it, and we are going to present one of our previous academic projects which applied the ML system to clarify how we applied it and our observations. The main target of the project was to study the urban morphology of the cities by GAN, nowadays we face growth of the world population so cities are growing rapidly to accommodate them, therefore cities morphology change. Instead of mimicking the master plans of developed cities we thought about using ML to predict new cities plans according to the population growth, so we tried to train a machine with supervised and unsupervised machine learning system to create new cities typology out of three different stages, 1st stage feed the machine with cities maps from global north, 2nd stage feed the machine with cities maps from global south, and the 3rd stage was to feed the machine with cities maps from global north and global south. Through figure 6.8 we can have a look to the flow diagram which shows the different steps of training the machine.

Figure 6.8: Work flow Diagram of ML to predict new cities morphologies out of global north and global south cities (Eka, Mohamed, & Yasin, 2020) Machine Learning Category

Class

Output

Samples

Unsupervised Learning

GAN

New hybrid of global cities. typology 2D Jpeg (512 px)

2D Jpeg (512 px)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 6.9: Sample of the global north cities which has been feed to the machine, Stara Zagora, Bulgaria

Figure 6.10: Sample of the global south cities which has been feed to the machine, Casablanca, Morocco

Figure 6.11: Sample of the trained model outcome in the 3rd stage which represent the combination between global north and global south cities (Eka, Mohamed, & Yasin, 2020)

Observation: •

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The initial results were not as expected because of the huge differences between the cities which made it complex at the beginning, then we had to make some classifications for the data samples, train the model more than one time and to run it for more iterations which consumed a lot of time but we got better results, As much as we feed the model with samples and run it for more iterations, we got better results. There were difficulties about getting the data which made using the machine quite hard, in addition to, we had to dig into another fields like programing to deal with the machine itself. It was outstanding to get such results out of the machine but in the same time it seems like artistic work not a real city master plan so it needs to work more on it or to revise the way of training the model again, to get results with a concrete scientific base.

6.4. Your future thoughts for the industry As we passed by industrial revolution, we are living in the revolution of information tech, world moves faster than before and we are facing more a lot of problems like climate change, so its crucial to find a quick solution to face that change. Machine learning and Artificial intelligent almost part of our daily life now, when we are typing or make our own research there are machines which learn and predict what kind of products, advertisements or videos could be proposed for us to watch, but what about human feelings and mode is it really something

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

machines could feel it or predict it, we think that this will be the next step even due to Covid 19, we had to change our life into digital style, which provide a fertile space for AI to grow faster than expected. When we talk about ML and architecture, we see that there are different categories in the industry which are going to be affected. Architecture design: it’s a bit complicated when we talk about design as it consists of different layers like function, society, and economy but machines will learn from the previous buildings to analysis the relations between zones and understand it, but it will not be limited to function only but machines will analysis and learn from user experiences to define what could be good or bad design. Machines will produce some proposals according to the analysis and the set of rules which has been defined by architects. Architects: AI could replace architects and manpower in some steps like analysis stages and producing some proposals according to this analysis and set of rules but at the end there will be a struggle between AI and human intelligence. In addition to, the change in the role of the architect itself will happen, instead of designing buildings will design the algorithms in the backend to allow machines dealing with the normal user. And a there will be a new generation of architects which will be experienced in other fields to deal with machine, also there will be a chance for others from outside the architecture field to invade the field. Tools and normal users: in the future tools will be developed in a way that it will have a user interface so the normal user can use it, maybe in small levels of designs like private home, normal users can deal directly with the machine to get their own design. Land management: according to the set of data which will be loaded to the machines to learn from it, machines will evaluate that land value and propose the probable use for the land to maximize to benefited from it. AI could be used in land managements, according the information, which machine has learned from it Construction: Machines will be partner in the construction industry, nowadays some software is using for coordination, expatiate the process of coordination and shows an error message so it helps architect to take care and to avoid the delay during construction but in the future some of these errors will be treated directly by software. And this leads to that some of the jobs will be not required anymore because it will be replaced, minimize the conflicts during construction phase, and because of the data base the decisions which will be taken during construction will be processed through material consumption, cost, and some other environmental factors. We think that ML will help to accelerate the construction process in order to keep pace with the world growth, but we are not sure about ML is it going to have a positive or negative effects on the people themselves? Will they lose their skills by relying entirely on AI or will this lead to themselves development in order to prove themselves?

7. Module V: Computational Biology Teacher(s): Prof. Liss C. Werner

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

7.1. What is in essence computational biology? The secret life itself is wrapped up in the mystery of genetic encoding and in the replication and motility of molecules that orchestrate each other's activity. Genes are information; molecules are media as well as motors, so to speak (Benedikt, 1994) Computational biology is an application of computer sciences, mathematical and statistical methods. It can be defined as a novel paradigm for computation where cells of living beings are taken as an example to define computational architectures and algorithms capable of solving problems efficiently and while being based on a low complexity description of its structure. In general, these applications are including data manipulations such as analyzing, recording, imaging, and visualization of genomic data processing (Dogaru, 2008). A living being can be considered as performing various natural computation tasks. While exhibiting complexity in performing various functional tasks (pattern recognition, decision, orientation, optimization, planning, creative thinking, self-repair, and self-reproduction, to name just a few) it is assumed that the entire development of the being is encoded within its genome. The goal of computational intelligence is to understand and exploit some basic principles of natural computing to construct various artificial systems capable to mimic some of the functions of living beings, through the simulation of cells motion, and interaction, for example, the simulation of the human brain’s cells motion, and interaction (figure 7.1) The human brain is a huge neural network in which billions of neurons interconnect through trillions of synapses. Benefiting from vast connectivity, functional organizational hierarchy, sophisticated learning rules, and neuronal plasticity, the human brain can simultaneously perform different complex tasks with massive parallelism, extremely low power consumption, superior fault tolerance, and strong robustness. (Yang, et al., 2020). In order to achieve functionality in computing systems, the gene information is unfolded in creating many similar cells in a basic hierarchy and these cells are naturally distributed so that each cell typically communicates with its immediate neighbors. The local connectivity is the essence of the cellular computing model, to be developed further (figure 7.2). In nature, besides nearest neighbors, also longrange connections are present allowing a small fraction of distant cells to communicate. While cell genes are interpreted to create the architecture of a natural computing system, an optimal number of basic cells forming a functional entity. (Dogaru, 2008).

Figure 7.1: shows the simulation of human brain’s neural network in artificial computing system. (Yang, et al., 2020)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 7.2: Shows the concept of a biology computing system, (Dogaru, 2008, p. 2)

7.2. How could Architecture/Urban Design Benefit from it? There are three levels of biomimicry that may be applied to a design problem are typically given as form, process, and ecosystem. In studying an organism or ecosystem, form and process are aspects of an organism or ecosystem that could be mimicked. The ecosystem however is what could be studied to look for specific aspects to mimic. There are three levels of mimicry; 1. Organism Level

2. Behaviour Level

3. Ecosystem Level

The organism level refers to a specific organism like a plant or animal and may involve mimicking part of or the whole organism. The second level refers to mimicking behavior, and may include translating an aspect of how an organism behaves, or relates to a larger context. The third level is the mimicking of whole ecosystems and the common principles that allow them to successfully function (Elsamadisy, Sarhan, Farghaly, & Mamdouh, 2019). Computational biology allows designers to grow buildings by programming cellular organisms to fabricate and deposit material into architecturally relevant patterns. In addition to developing form-finding computer software that simulates simple biological morphologies and enables us to manipulate and intervene in the form-making process through an editor interface. The design strategy hints at the production of biologically engineered architecture, which would potentially behave as an ever-changing organism. The cell behavior can be molded in microbial communities, specifically related to cell growth and distribution. In addition, we can explore a secondary process of biomineralization through a combination of software and three-dimensional printing. The basic implementation of Boid rules includes a set of three behaviors: (a) separation, the condition to keep separated from other particles; (b) alignment, an average direction based on the state of neighboring agents; (c) cohesion, an immediate reaction in position based on neighbors’ direction (figure 7.3). SynthMorph, is a form-finding software that operates under dynamic biological constraints. The software primarily models cell behavior in microbial communities, specifically related to cell growth and

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

distribution, Figure (7.4). In this software the cells have four types of controllers. The first control modifies the number of cells within the community, thus simulating duplication. This behavior is codified as an increment in the number of cells, with new cells being placed at the same coordinates as the existing ones. A second control was codified to control the distance between cells to avoid collision. The third control activates the random walk function, which is analogous to the function of motility in real cells. Finally, the fourth control allows users to reset the position of all cells. Globally, the system was controlled using an iteration counter. Given the time dependency of all biological systems, a display of the time condition within the system was implemented in the graphical interface. Users can know exactly at which point in the simulation the system is currently at, and they can program the system to stop at a particular iteration the influence of attractors over cell distribution. The spread of cells shows that each attractor generates an area of influence that approximates the shape of a perfect sphere. The Area of Influence variable determines the radius of the sphere of influence, whilst the attractor strength determines how fast each cell is pulled toward the attractor figure (7-5). (Ramirez-Figueroa, DadeRobertson, & Hernan, 2013).

Figure 7.3: Shows the basic morphogenetic effectors. (Ramirez-Figueroa, Dade-Robertson, & Hernan, 2013, p. 53)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 7.4: SynthMorph shows randomly positioned attractors (Ramirez-Figueroa, Dade-Robertson, & Hernan, 2013, p. 45)

Figure 7.5: Shows Geometry derived from cell behavior using SynthMorph. (Ramirez-Figueroa, Dade-Robertson, & Hernan, 2013, p. 58)

Computational biology also allows the designers to transfer their concepts from bio-digital form-driven architecture to digital-biological behavior and material-driven architecture, through using the organism’s behavior in a global regional, and local scale of architecture and urban design. This approach combines morphology, structure, infrastructure, and metabolism; its algorithmic and parametric design strategies for architectural optimization and computational urban planning for a lean networked cognizant architecture (figure 7.6). (Werner, 2018).

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 7.6: shows global, regional and local morphology. The primary branch which consolidates into the ‘shortest path’ and main infrastructural link forming the edges of each cell in the Voronoi typology. (Werner, 2018, p. 6)

7.3. Your reflection of benefit of the topic From our previous work, we worked on a project which was designing a kinetic façade shading unit, this shading unit was supposed to be based after a unique system or behavior of the surroundings. The main idea was to investigate into rules and behaviors to generate a system that can be then translated into a mechanism, material or a specific response to certain elements, and finally integrating all of this into a form-finding process to be able to come up with our kinetic shading unit as an output.

Figure 7.7: Diagram showing the main process of creating the kinetic shading element (Own diagram)

Our concept for this project was to mimic the sunflower behavior towards sunlight, by creating a similar mechanism. We started by abstracting the flower seed shape and then converting the sun growth movement to a similar rotation motion to open and close.

Figure 7.8: Images Showing the mimicking of the form and shape of the bud (Omar Fouda previous work)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

After creating our form and shape, it is time to design the structure mechanism, the mechanism was based on sensor motors which run by electricity to open and close the unit

Figure 7.9: Illustrations showing the structure mechanism of the unit (Omar Fouda previous work)

Figure 7.10: Image shows the unit full structure on a façade (Omar Fouda previous work)

The reflection of benefits of computational biology can come here in the form of material investigating and exploration, where instead of creating a full industrial model, we could have used a natural bio material for lower carbon effects and for a certain life time, materials may also behave in the same way if applicable without using any energy.

Figure 7.11: Image Shows the unit material and how mimicking can go further towards exploring and abstracting properties (Omar Fouda previous work)

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 7.12: Images show the behavior of the mechanism system towards motion sensor (Omar Fouda previous work)

7.4. Your future thoughts for the industry Perhaps the buildings in the future will not need to be artificial intelligence filled structures that are mapping our every move. Maybe they need to be responsive structures infused with a bit of biology and self-sustainable. North Umbria University Newcastle developed a unique experiment of ‘Living’ House, Figure7.8. This experiment aimed to develop biotechnologies to construct a new generation of Living Buildings that are responsive to their natural environment. These buildings will be grown using living engineered materials to generate energy, reduce inefficient industrial construction processes, reducing pollution, metabolism their own waste, and modulate their microbiome which benefit human health, wellbeing, and ecological health. These building design concepts include four themes as living construction, microbial environments, building metabolism, and responsible interactions (figure 7.9). Another thought for the future of this industry, many organisms can be considered as nature’s engineers as birds that have their own engineering in constructing their nests (figure 7.10). Perhaps in the future the behavior of these birds will be adapted and changed through genetic changes to make these birds able to construct our buildings and cities on a small scale, and then by using artificial intelligence it will be able to fabricate them.

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 7.13: Show the experimental of biological house. (Source, A living breathing building: How biology and architecture will change construction and the built, https://archinect.com/news/article/150147904/a-living-breathing-building-how-biology-and-architecture-willchange-construction-and-the-built-environment

Figure 7.14: Shows the living house experiment themes. source, A living breathing building: How biology and architecture will change construction and the built, https://archinect.com/news/article/150147904/a-living-breathing-building-how-biology-and-architecture-willchange-construction-and-the-built-environment

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

Figure 7.15: shows Bird Nests Engineering. (Nature’s Engineers: Bird Nests | The Transient Biologist, https://thetransientbiologist.wordpress.com/2014/07/09/natures-engineers-bird-nests/,Accessed 3-7-2021)

8. Conclusion Within the Artificial nature I seminar we studied five modules Computational thinking, computational tool, computational urban analysis, Machine learning, and computational biology and we represent our thoughts within the report through showing the essence and how it affects on the future of the industry, each module has effects and it could be positive or negative but it depends from which approach we look into it, If we look into computational tolls from the perspective of architects we can say that it’s beneficial and it will help to us for more innovative design and safe time, but at the same time maybe part of architects will see that it needs more effort. If we look into it from different perspective like draftsmen maybe they will see it in a negative way because it could threat them to lose their work. So, it is clear that what can be good for one is bad for the other, it only depends on which side you are. We live in the ere of information technology and AI, machine was tool, we use it to do our work for the process acceleration but it seems that the role is going to change and it will be our partner for the future and invade more space in our life, and this could lead us to lose our skills or it will force us to develop our skills in order to prove that we are still here.

Al-Ashwah, Fouda, Hegaz, Artificial Natures I, Decoding the Future of Architecture and Urban Design between Computational Thinking and Machine Learning, 2021

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Okhoya, V. (2014). TOWARDS THE APPLICATION OF MACHINE LEARNING ON ARCHITECTURAL PROJECTS. Ramirez-Figueroa, C., Dade-Robertson, M., & Hernan, L. (2013). Adaptive morphologies: Toward a morphogenesis of material construction. CUMINCAD. Somvanshi, M., Chavan, P., Tambade, S., & Shinde, S. (2016). A review of machine learning techniques using decision tree and support vector machine. In 2016 International Conference on Computing Communication Control and automation (pp. 1-7). IEEE. Werner, L. C. (2018). Biological Computation of Physarum-From DLA to spatial adaptive Voronoi. Wooley JC, L. H. (2005). Catalyzing Inquiry at the Interface of Computing and Biology. Washington (DC): National Research Council (US) Committee on Frontiers at the Interface of Computing and Biology. Wortmann, T., & Nannicini, G. (2016). Black-box optimisation methods for architectural design. Yadav, A., Good, J., Voogt, J., & Fisser, P. (2017). "Computational thinking as an emerging competence domain". In In Competence-based vocational and professional education (pp. 1051-1067). Springer, Cham. Yang, J.-Q., Wang, R., Ren, Y. a.-Y., Wang, Z.-P., Zhou, Y., & Han, S.-T. (2020). Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems. Advanced Materials, 32(52), 2003610. Zhang, H., & Blasetti, E. (2020). 3D Architectural Form Style Transfer through Machine Learning.

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