Data Tectonics by Georgios Plakotaris

DATA _ TECTONICS

Research Cluster 1

Dataâ&#x20AC;&#x201C;tectonics Virginia Giagkou Sisi Li Georgios Plakotaris Zihan Yu Jinjin Zhao

Tutors: Daghan Cam, Martina Rosati, Davide Quayola, Alexandros Kallegias

Contents 00

Introduction

Project statement

Input and output

Theoretical framework

Cellular Automata

Practical framework

Force field with different ranges

Data analysis

Force field with different weight maps

105

Types of data that we collect

Ten examples of different cliffs

Data visualization

Generative System

Our environment

The forcefield

Cellular Automata

The steps of the generative system

Process breakdown

Optimization

Adaptability A Different top limits

113

B Different Cliffs

119

Fabrication

Materials and geometry

143

Assembly line

145

In the laboratory

153

Applications

157

Areas of previous research

171

Introduction

Figure 1. Automated manufacturing. Germantown, USA

During this academic year, we investigate the outskirts of cutting-edge architecture and design, with an accentuation on the most recent technological advances, especially computation and robotics. The design is an essential agency for revealing complex examples. So, we need to pick up a comprehension of the part computation plays in complex plan amalgamation. More specifically, we use the latest technological advances in data science and exploit them by analysing and adapting them for a design system characterized by autonomy in spatial performance and automation in assembly mode. This cross development is profoundly changing the part of the architectural engineer and designer. According to this, we will intend to make autonomous systems that can adjust to complex prototypical situations of design. We will center on outlining frameworks that can self-produce effective design structures utilizing techniques from the rising field of data science. Rocky landscapes, loaded with rich geometric information because of their multifaceted nature, are considered to be halfhabitat. Therefore, a metal structure is proposed with observatories and spaces that can be inhabited. This architectural composition is characterized by autonomy and, at the same time, poses new perspectives for the design sector as it adopts spatial data and is formed accordingly. In addition, a construction program is recommended, promoting the collaboration of robots with humans through a semi-automated rationale.

Figure 2. Daata editions, Davide Quayola

Figure 3. Google A.I. teaches itself to walk

Figure 4. First graphic representation

Analysis and processing of data are not new and is related to the development of these methods of visualization. The idea of displaying data - that is to converting numbers to illustrations to make them widely understood - seems to have started in 1786. William Playfair made public the first graphic representation of an Atlas related to Scottish trade. Of importance is the use of bars depicting trends in each value. Figure 4 In addition, the following map also played a major role in data visualization, since it transformed the way we perceive epidemics into urban environments. More specifically, in London, in 1854, a remarkable cholera epidemic emerged. After confirming that it was due to contaminated water, the premises of the area in which individual victims of this disease were recorded were mapped. Figure 5 By the 1950s, newspaper readers had become familiar with the use of graphs and other visual representations and the information in the data was successfully perceived. Nowadays, charting has evolved drastically and has set new rules on how experts can communicate complex and large volumes of data. From turning the data into games and intelligent exercises, until you convert large numbers into relevant representations, this visualization aims to become more interactive with the recipient, to make it a participant of the information and to detach from his passive attitude, causing him to obtain the information himself. Accordingly, returning to our surfaces, we take the advantage of their characteristics, trying to translate them into spatial data, such as numbers and vectors. To evaluate these values, we use a color map and a vector map system.

Figure 5. Map of infected residences

Figure 6. Berlin at night, 2016

Figure 7. Germany, Belgium and Netherlands, 2016

There is the possibility to perceive a surface as a topography of any scale when dealing with the recording and analysis of its data. A representative example demonstrated by this is the satellite images of the Earth during the night, where the amount of artificial light emitted could be studied. This simple recording is directly applicable to a variety of fields such as urban planning, sociology, geopolitics, forensics and environmental pollution. On a first level, the maps of night artificial light on Earth compose an interesting indicator of the phenomenon of increasing urbanization and ecology. Still, continuous recording can prevent the consequences that natural disasters can cause through a careful process of composing the image from a region that was destroyed and destroyed. The future of remote sensing technology will be able to provide more accurate data to even greater resolution than today, thus managing a real-time response to astro-spatial design. Data analysis is characterized by scalability as it expands the fields the architect can design and offer. Following the analysis of the surficial data, there is a need of using this information by reflecting them to space in between the two topographies that enclose our environment. If the negative space between the two surfaces is considered as a deactivated area, then our data are going to fill it with vectors or forces. The idea of forces came from the main goal of our concept; to create an autonomous system that can adapt and compose over rocky environments. That vertical relationship and at the same time these connections that are based on physical laws are the main spatial meanings that lead us to use the form of vectors. So, we use the analysis of the given data to create a new universe of data, which is consists of magnitudes and directions. These new data will be smoothly transited from the one to the opposite surface, combining their characteristics. To accomplish this objective, we have subdivided the space that is defined by the rocky surface and the upper limit into voxels, so we can store the information of our unique circumstance. Every one of these subdivisions takes a vector with particular data based on the separation between the two surfaces and the selected information. This is the initial step in making a field of forces. We need to accomplish a reflection of the properties that the two surfaces have and associate them or separate them, consolidating information and producing new ways. Each voxel stores one or more forces, generated by specific data. In this way, we create a dynamic field of forces, shaping the space by the needed directionality and tension.

Figure 8. Puerto Rico before and after hurricane Maria. 2728 September 2017

Figure 9. left side: representing the quantity of the Rho Oph dark cloud right side: efficient varieties of the far-infrared polarization force field in the interstellar cloud Rho Ophiuchi.

Figure 10. Human pose detection. Machine Learning

Figure 11. Cover for El PaĂs Semanal about self driving vehicles.

Project statement

We use the latest technological advances in data science and exploit them by analyzing and adapting them for a design system characterized by autonomy in spatial performance and automation in assembly mode. This cross development is profoundly changing the part of the architectural engineer and designer. According to this, we intend to make autonomous systems that adjust to complex prototypical situations of design. We centre on outlining frameworks that can self-produce effective design structures utilizing techniques from the rising field of data science. Rocky landscapes, loaded with rich geometric information because of their multifaceted nature, are half-habitat. Therefore, a metal structure is proposed with observatories and spaces that can be inhabited. This architectural composition is characterized by autonomy and, at the same time, poses new perspectives for the design sector as it adopts spatial data and is formed accordingly. In addition, a construction program is recommended, promoting the collaboration of robots with humans through a semi-automated rationale. The project is distinguished by the following interrelated steps, starting from the collection of data from the rocky area and ending up again for the assembly of the metal elements. Drones collect the necessary data by scanned the area and then send all the data to be processed on the host computer. The data input to the computer is analyzed based on the initial parameters so that the production of the design system begins. Thereafter, data is extracted, and the performance of the result is recorded. By using optimization methods, a better performance of the design system is approached, and these geometries are assumed for their implementation. Vehicles loaded with metal rods and joints transport the material to the area, and robotic arms assemble each paver in conjunction with the human resource that places the two-piece joints. Then, cranes place the objects in their final position on the rocky ground.

Figure 12. Top view of a cliff

Figure 14. Data Analysis of the direction of the slope. (Top view)

Figure 13. Data Analysis of the slope. (Top view)

Figure 15. Data Analysis of the normal vectors. (Top view)

Figure 16. The force field. (Perspective view)

Figure 17. Color coding of the generative system. (Perspective view)

Figure 18. Our digitall result. (Perspective view)

Figure 19. Our fabrication method. (Perspective view)

Theoretical framework

Figure 20. Pattern â&#x20AC;&#x201C; Structure (Perspective view)

Figure 21. Two abstract surfaces consists an environment

The interpretation of a domain's data to make a structure that can adjust to the information it experiences characterizes the design procedure. Two abstract surfaces consist the environment with measurements of 20 to 20 meters. They are placed vertically, one above the other, at a combined distance of 15 meters, but the height is flexible. These surfaces are analyzed based on various geometrical parameters. These parameters are the input data that set the first design principles. The curvature of the surface, the height, depth, and width ratio, how sharp the surface is, and how much and which is noisy are recorded. These parameters are translated into decimal numerals from zero to one, and are visible through the color spectrum, from blue to red respectively. Also, parameters that are translated into vectors are used, as their information also includes the concept of direction. These are the normal vectors and the set of vectors that indicate the directions to the high and low areas. So, we are called upon to design a spatial system that supports the upper surface, utilizing data on both surfaces.

Figure 22. A magnetic field created by two metallic bars

Figure 23. Vector representation of interstellar forces

Figure 24. A monastery on Amorgos, Greece

Figure 25. A monastery on Meteora, Greece

The research mainly refers to scalable systems and shapes that can be created from different fields of forces. For example, cross scaling similitudes are seen between a discovery of a magnetic field that offers pieces of information to universe arrangement forms and a magnetic field of linear particles that is shaped from two attractive bars. If these streaming powers are spatial vectors that can set the progression of our generative framework, at that point their geometrical properties of the vectors, for example, size and direction could be utilized to make our loaded with interpreted information canvas. As this force field delivers some surficial data outwardly, it separates the space in between into dynamic areas of different measure and direction. In these areas, linear elements of the design are oriented, which in the process of fabrication will be translated into metallic rods. Tension and compression, the two forces of life manifested in that single material.

Figure 26. One meter wide wooden road on a vertical cliff

Figure 27. Housing complex of Himalaya, India

Figure 28. Orbit tower, London

Figure 29. Eiffel tower, Paris

In the same way, systems and forms of metallic structures have been studied, so the generative system could be transformed into a materialized space, like steel, that is widely used in architecture. Inspired by the fragmented space that scaffoldings are creating, there is a willing for understanding the altered qualities of space that a massive metallic structure makes and at the same time keeping the perception of a light structure. According to this, there is no matter of how much the needed to construct mass will be and the scale that refers to in a public space, like Eiffel and Orbit tower in Paris and London, respectively. Combined with the use of proximity rules from Cellular Automata's automated logic, oriented linear sections are positioned to form desirable patterns. Taking advantage of the directionality of the force field, they fill the space between the horizontal planes, completing the neighbourhood rules. The composition is then finished by vertical components put based on reasonable requirements concerning the most extreme conceivable associations by collecting discrete bars and connectors. The same procedure is followed in two smaller scales, to achieve adequate support for viable and accessible spaces in general.

Figure 30. Eiffel tower, Paris

As it is widely known, the term data science came to prominence in the late 1990s in discussions relating to the need for statisticians to join with computer scientists to bring mathematical rigor to the computational analysis of large data sets. The science of data can be enriched, let alone transform the way architects deal with almost all design problems. Access to an infinite volume of data as well as direct processing through artificial intelligence are the main factors that open new horizons in the architectural process. Sometimes these horizons hide some dark points when making architectural decisions. The architect should not be limited by the predictions that arise from the analysis of the data. Artificial Intelligence is not accidentally called artificially. It is complementary intelligence and bridges the gap between architectural decision and spatial function. It would be good to remain a tool and not replace the human factor. As machines evolve through technology that is produced by people, so architect-people need to enrich their approach and think like machines. So, the gap between creator and creature is probably bridged. The designer is called upon to implement methods and tactics that could not even be imagined without the possibility of computers. Artificial Intelligence may be the vehicle that, whenever technology permits, is transformed into a fast machine, but the architect is the one who is called upon to handle it, lead it and at the same time discover new streets and worlds, digital or real.

Figure 31. Workers on a scaffolding.

Figure 32. Detail of a scaffolding

Practical framework

The design procedure could be portrayed as reliant upon heuristic thinking, since all outline challenges are unpredictable and to some degree open-finished with vague definitions and uncertain endpoints, borrowing from sociologist Horst Rittel who characterized these as “wicked problems”. Machines themselves aren't, in any event, today, especially great at heuristics or taking care of mischievous issues, yet they are progressively fit for requiring the administration of intricate, interconnected quantitative factors like manageable execution, development coordination, and cost estimations. What's more, since customers have a solid enthusiasm for seeing those things done well, the opportunity to supplement heuristics will incline toward and use the subsequent incentive accordingly. That designers are so appropriate to the difficulties of the underhanded issue looks good for us in the ‘Second Machine Age’, as it is called when computers don't simply do things we program them to do however can figure out how to do new things.

Figure 33. Drones collect the data of the cliff

Figure 34. Data analysis. Generative system. Optimization. Fabrication.

The basic estimation of designers as experts who can comprehend and assess an issue and incorporate remarkable and shrewd arrangements will probably stay unchallenged by our machine partners in the near future. More particularly is recommended that â&#x20AC;&#x2DC;occupations that involve complex perception and manipulation tasks, creative intelligence tasks, and social intelligence tasks are unlikely to be substituted by computer capital over the next decade or two.â&#x20AC;&#x2122; (Bernstein, 2017) In any case, designers' calling must grasp and assault the fiendish issue without bounds of engineering and computational outline and control the eventual fate of their calling as needs are.

Figure 35. The assembly takes place on site

Figure 36. Detail of the assembly. Semi-autonomous system

Data analysis

Types of data that we collect

The surfaces became more specific with clear geometric characteristics that produce a set of realistic data, which are related to the purpose of this work, the parametric design of a metallic structure in a rocky environment. Five different cases of stone surfaces were studied, which are distinguished by their complexity, namely the geometric noise, as well as their general geometry, as their wider shape and form. As can be seen in the diagram below, all geometries are delimited by an imaginary base box 60 by 60 meters and a height of 20 meters. In these, their central area was defined as an area of intervention 20 by 20 meters. The first surface is defined by a general sloping mountain surface with a strong slope and a smaller one opposite it. The latter forms a three-quarters wide hollow section with a strongly inclined behaviour. The Third Surface follows the general geometry of the aforementioned, but with greater complexity. More specifically, it displays more fluctuations of local maximum and minimum than the previous one, which is evident when its generalized surface is observed. The fourth and the fifth surface create a ravine in the central areas of their area, and are mainly differentiated in its geometry; in the fourth, it has the shape of a slightly curved line in a top view, while on fifth it forms a branch and spreads over two sections and is divided into three general hills. It is obvious that research should not focus on the design of mountainous surfaces as their production is endless. However, the mining of the data that follows will give us some initial conclusions regarding design projectâ&#x20AC;&#x2122;s ability to adapt to each landscape.

Figure 37. Data Analysis

We perceive the surfaces as a topography made of points with specific coordinators. Each point corresponds to a spatial information denominated at a numerical value between 0 and 1, clearly maintaining its relative proportions with the other points. Then it is helpful to colorfully declare this decimal arithmetic information. A color matching between blue is selected, which has an arithmetic correspondence (0, 0,255) in logical r-g-b and the red with r-g-b logic the relation (255, 0, 0). So, we visualize the color shades that arise between blue (as a minimum) and red (as the maximum). So, we calculate the relative position of each point based on the three coordinates x, y, and z. Equally important is the curvature of each point calculated in relation to the vertical distance of adjacent points as well as the slope and roughness that record whether a sharp one is an area or with intense differences between neighbors, respectively. Another approach is that of data mining associated with vectors. A typical example is the normal vectors, which are all the vectors with same length, which are vertical at the respective tangent levels of each point in the topography. The plurality of vertical information enables us to perceive the behavior of the rocky surface in the direction it may have, its degree of complexity, whether it is abrupt or not. Through vectors, we can also point to the high or low areas respectively. Thus, we can calculate the paths they should make, for example by gravity, on the surface or slope direction.

Figure 38. Color mapping for vertical position

At this point, it is necessary to predict the use of these data and their exploitation for the design. There are many approaches when you think of all the possible combinations of the above data, whether they are color rendered 'arithmetic' or vectors. Ideally, we could use low slope regions, that is, those that are less steep, to build our structure while using the normal vectors direction, but this can limit the possibilities for autonomy. Thus, as will be described below, it is desirable to transfer the data from the surfaces to their interspace through a field of forces that will lay the bases for the autonomous design system. Figure 39. Color mapping for slope

Figure 40. Vector mapping for the direction of slope

Figure 42. Isometric view of a cliff

Figure 41. Vector mapping for the normal vectors Figure 43. Color mapping from blue to red

Data analysis

Ten examples of different cliffs

Figure 44. Isometric view of cliff 01 and of its examined area

Figure 45. Isometric view of cliff 02 and of its examined area

Figure 46. Isometric view of cliff 03 and of its examined area

Figure 49. Top view of cliff 01 and of its examined area

Figure 50. Top view of cliff 02 and of its examined area

Figure 51. Top view of cliff 03 and of its examined area

Figure 47. Isometric view of cliff 04 and of its examined area

Figure 48. Isometric view of cliff 05 and of its examined area

Figure 52. Top view of cliff 04 and of its examined area

Figure 53. Top view of cliff 05 and of its examined area

Figure 54. Isometric view of cliff 06 and of its examined area

Figure 55. Isometric view of cliff 07 and of its examined area

Figure 56. Isometric view of cliff 08 and of its examined area

Figure 59. Top view of cliff 06 and of its examined area

Figure 60. Top view of cliff 07 and of its examined area

Figure 61. Top view of cliff 08 and of its examined area

Figure 57. Isometric view of cliff 09 and of its examined area

Figure 62. Top view of cliff 09 and of its examined area

Figure 58. Isometric view of cliff 10 and of its examined area

Figure 63. Top view of cliff 10 and of its examined area

Data analysis

Data visualization

Surface â&#x20AC;&#x201C; Cliff 01 Figure 64. Front view of vertical position

Figure 67. Front view of slope

Figure 65. Left view of vertical position

Figure 68. Left view of slope

Figure 66. Top view of vertical position

Figure 69. Top view of slope

Figure 70. Front view of normal vectors

Figure 73. Front view of direction of slope

Figure 71. Left view of normal vectors

Figure 74. Left view of direction of slope

Figure 72. Top view of normal vectors

Figure 75. Top view of direction of slope

Data analysis

Data visualization

Surface â&#x20AC;&#x201C; Cliff 02 Figure 76. Front view of vertical position

Figure 79. Front view of slope

Figure 77. Left view of vertical position

Figure 80. Left view of slope

Figure 78. Top view of vertical position

Figure 81. Top view of slope

Figure 82. Front view of normal vectors

Figure 85. Front view of direction of slope

Figure 83. Left view of normal vectors

Figure 86. Left view of direction of slope

Figure 84. Top view of normal vectors

Figure 87. Top view of direction of slope

Data analysis

Data visualization

III Surface â&#x20AC;&#x201C; Cliff 03 Figure 88. Front view of vertical position

Figure 91. Front view of slope

Figure 89. Left view of vertical position

Figure 92. Left view of slope

Figure 90. Top view of vertical position

Figure 93. Top view of slope

Figure 94. Front view of normal vectors

Figure 97. Front view of direction of slope

Figure 95. Left view of normal vectors

Figure 98. Left view of direction of slope

Figure 96. Top view of normal vectors

Figure 99. Top view of direction of slope

Data analysis

Data visualization

IV Surface â&#x20AC;&#x201C; Cliff 04 Figure 100. Front view of vertical position

Figure 103. Front view of slope

Figure 101. Left view of vertical position

Figure 104. Left view of slope

Figure 102. Top view of vertical position

Figure 105. Top view of slope

Figure 106. Front view of normal vectors

Figure 109. Front view of direction of slope

Figure 107. Left view of normal vectors

Figure 110. Left view of direction of slope

Figure 108. Top view of normal vectors

Figure 111. Top view of direction of slope

Data analysis

Data visualization

V Surface â&#x20AC;&#x201C; Cliff 05 Figure 112. Front view of vertical position

Figure 115. Front view of slope

Figure 113. Left view of vertical position

Figure 116. Left view of slope

Figure 114. Top view of vertical position

Figure 117. Top view of slope

Figure 118. Front view of normal vectors

Figure 121. Front view of direction of slope

Figure 119. Left view of normal vectors

Figure 122. Left view of direction of slope

Figure 120. Top view of normal vectors

Figure 123. Top view of direction of slope

Data analysis

Data visualization

VI Surface â&#x20AC;&#x201C; Cliff 06 Figure 124. Front view of vertical position

Figure 127. Front view of slope

Figure 125. Left view of vertical position

Figure 128. Left view of slope

Figure 126. Top view of vertical position

Figure 129. Top view of slope

Figure 130. Front view of normal vectors

Figure 133. Front view of direction of slope

Figure 131. Left view of normal vectors

Figure 134. Left view of direction of slope

Figure 132. Top view of normal vectors

Figure 135. Top view of direction of slope

Data analysis

Data visualization

VII Surface â&#x20AC;&#x201C; Cliff 07 Figure 136. Front view of vertical position

Figure 139. Front view of slope

Figure 137. Left view of vertical position

Figure 140. Left view of slope

Figure 138. Top view of vertical position

Figure 141. Top view of slope

Figure 142. Front view of normal vectors

Figure 145. Front view of direction of slope

Figure 143. Left view of normal vectors

Figure 146. Left view of direction of slope

Figure 144. Top view of normal vectors

Figure 147. Top view of direction of slope

Data analysis

Data visualization

VIII Surface â&#x20AC;&#x201C; Cliff 08 Figure 148. Front view of vertical position

Figure 151. Front view of slope

Figure 149. Left view of vertical position

Figure 152. Left view of slope

Figure 150. Top view of vertical position

Figure 153. Top view of slope

Figure 154. Front view of normal vectors

Figure 157. Front view of direction of slope

Figure 155. Left view of normal vectors

Figure 158. Left view of direction of slope

Figure 156. Top view of normal vectors

Figure 159. Top view of direction of slope

Data analysis

Data visualization

IX Surface â&#x20AC;&#x201C; Cliff 09 Figure 160. Front view of vertical position

Figure 163. Front view of slope

Figure 161. Left view of vertical position

Figure 164. Left view of slope

Figure 162. Top view of vertical position

Figure 165. Top view of slope

Figure 166. Front view of normal vectors

Figure 169. Front view of direction of slope

Figure 167. Left view of normal vectors

Figure 170. Left view of direction of slope

Figure 168. Top view of normal vectors

Figure 171. Top view of direction of slope

Data analysis

Data visualization

X Surface â&#x20AC;&#x201C; Cliff 10 Figure 172. Front view of vertical position

Figure 175. Front view of slope

Figure 173. Left view of vertical position

Figure 176. Left view of slope

Figure 174. Top view of vertical position

Figure 177. Top view of slope

Figure 178. Front view of normal vectors

Figure 181. Front view of direction of slope

Figure 179. Left view of normal vectors

Figure 182. Left view of direction of slope

Figure 180. Top view of normal vectors

Figure 183. Top view of direction of slope

Generative system

Our environment

Figure 184. Division of the cliff to its upper projection

Figure 185. Projection of the top points of the grid on the cliff

Figure 186. Collection of the higher points of each group

As mentioned in the introduction, the design system is surrounded and is initially defined by the intermediate space enclosing two opposing surfaces. If the lower surface is a rocky area as we have seen, then the top surface is dependent on it and arises through predetermined algebraic parameters without its predetermined shape as will be discussed below. It is also worthwhile to mention that the upper surface does not translate into surface material covering the whole of the structure but is the upper limit that is the phrase of the extension area of the design. Figure 187. Mark points in between with a stable distance

Figure 188. Projection of the top group to the desirable height

Let's analyse the top surface design process in the following steps: 1. The base of the rocky surface is divided into equal square blocks, depending on the desired grid analysis. 2. Î&#x2018; range of points belonging to the rock corresponds in each square segment. From the set of these points we select the highest point, and then we rise from this square section to the corresponding vertical distance. Figure 189. 4 sq.meters groups above 4 meters of the cliff

3. Thus, a set of identical square blocks has been produced that will read the general geometry of the rock. We could say that if we degraded the grid's analysis to the maximum (each square segment of the grid corresponds to each point of the rock) then we would produce as a top surface a faithful geometry of the rock. In any case, in order to smooth out the minimal variations that may arise, we set a Euclidean division, and so the surfaces could be grouped based on the remainder they give. The result of this method is to generate more generalized areas of the same height, giving one more tool to control any desirable fracture.

Figure 190. 4 sq.meters groups above 6 meters of the cliff

4. The combination of different results based on the data analysis explained below gives the geometry of the total surfaces that make up the upper boundary of the structure.

Figure 191. 4 sq.meters groups above 8 meters of the cliff

Figure 192. Different groups with different heights above different cliffs

Generative system

The forcefield

Generative system

The forcefield

Having analyzed the data of the topography at the first step of the design process, at the next step we focus on the space-in-between the area of intervention. The goal is to diffuse the data of the landscape in the threedimensional space, to create a dynamic, information-rich environment. In order to achieve that, we create, based on the selected data, a field of forces in space. These forces are perceived as the directional forces, the active information that will drive the system. The idea of the force-field was devised through a series of references and inspirations. We observed similarities with a galactic magnetic field, which through its internal forces generates and directs new galaxy formations. We got inspired by the tension between two magnets that directs the distribution of matter between them. At the same time references such as fluid dynamics, the Navier-Stokes equations, that describe the movement of viscous fluids based on the fluctuations of the velocity and pressure of the flow, strengthened even more the notion of directionality and tendency that can influence a system.

We could also refer to the movement of the silt inside a river. As the water of the river flows, the matter seems to follow its flow, the tendency and directionality imposed by the river. Similarly, to the examples mentioned previously, spatial vectors generated by the data of the two surfaces can set the dynamics and direct the generative system. We use the vectors of the input data to generate the magnitude and direction of the vectors of the inbetween field. Each surface reflects its data and information in the inbetween space in a range. In this way a smooth transition from the data of the landscape to the data of the upper surface is achieved. Reflecting the properties of the two surfaces in the in-between space we can combine data and shape the space by the needed directionality and tension.

Figure 193. Perspective view of the force field

Figure 194. Data visualization of the vertical vectors

Figure 200. Force field of the vertical vectors

Figure 197. Division of the space into voxels

Figure 198. Closest distance to point up and point down

Figure 195. Data visualization of the normal vectors

Figure 201. Force field of the normal vectors

Figure 199. Get more influenced from the closest distance

Figure 196. Data visualization of the direction of slope

Figure 202. Force field of the direction of slope

The steps of the force field

To generate the forcefield, at first, we subdivide the space between the two surfaces into voxels, so we can store the data of the context. Each voxel stores a series of information needed for the system to evolve, including a few vectors corresponding to the number of data analyzed for the two surfaces. Each vector assigned to each voxel constitutes a weighted sum of two vectors, which correspond to the data of the two different surfaces. Each voxel stores the information of two points (one for each surface) that lie in the closest distance above and below its position. The resulting value of the direction and magnitude of the vector assigned to the voxel is mostly influenced by the point closer to its position. Thus, a field of forces for each data map is generated. Step 1. First type of forces based on the ycoordinate of the points of the surfaces in space. At the first step, we focus on the different heights of the points of the surfaces. We collect the information of the y position of the points of the two surfaces. Each voxel stores the information of two points (one of the lower and one of the upper surface), that lie in the closest distance beneath and above the position of the corresponding voxel. The magnitude of the force, stored in the voxel, results from the weighted average of the y position of the two closest points and gets more influenced by the data of the surface that is in higher proximity to the voxel. Furthermore, the forces become weaker or stronger, as their position gets further away or closer to the surfaces respectively. This way we can achieve the gradual transition from the influence of the lower to the influence of the upper surface. The direction of the forces is vertical, starting from the lower and heading towards the upper surface. Thus, the first forcefield reflecting the altitudes of the landscape is created.

Step 2. Second type of forces based on the normal vectors of the two surfaces. At the second step, we collect the data of the normal of the points of the two surfaces. Each voxel contains a vector that arises from the combination of the information of two points, the one that lies in greater proximity beneath and the one that lies in greater proximity above its position. The magnitude of the forces is decreased as the corresponding voxel’s position gets further away from its closest surface. This way the influence of one surface gradually fades in the influence of the opposite surface. The direction of the second set of forces follows the direction of the normal vectors of the surfaces. The second forcefield reflecting the normals of the landscape is created.

Step 3. Third type of forces based on the direction of slope. At the third step, we focus on the vectors concerning the direction of each point of the surfaces to the neighbor of the highest altitude difference in the point’s proximity. We collect the information of the direction and magnitude of the vector for each point. Each voxel contains a vector generated based on the weighted average of the data of the two points that lie in greater proximity beneath and above its position. The magnitude of the force depends on the height differentiations of the points of the surface. At the same time the magnitude is also influenced by the voxel’s distance to its closest surface. The force gets weaker or stronger as its position gets further or closer to the surface respectively. The influence of the two surfaces seem to gradually fade towards the center of their distance. The direction of the forces is also influenced by the geometry of the surface, as it heads every time towards the highest areas of the surface. The third forcefield reflecting the direction of slope of the landscape is created.

Figure 203. Cliff 01

The final forcefield Having generated one forcefield for each datamap created, the final force field is produced by the weighted sum of the N primary force fields as follows:

Figure 204. Force field front view

where Fv is the force of the final force field on the position of voxel v, Fi,v is the force of the i-st primary force field on the position of voxel v, Wi is the weight by which all forces of the i-st primary force field are multiplied before added up to produce the final forces Fv.

As we can see, the generation of the forcefield gives us the opportunity to take full advantage of the data extracted from the surface’s geometry and use it as the input of our system, the driving force of the design process. Through the creation of the field we manage to translate and transfer the selected information into space. In this kind of space, a system can develop step by step, adapting each time to the data it encounters. The forcefield is adaptable, scalable and dynamic. It can adapt to any data, to any surface. It can respond to a microscale and up to a macroscale and finally it comprises of dynamic forces of tendency and tension. Its adaptable nature allows us to fully explore the possibilities of the data in the design system. Figure 205. Force field left view

Figure 206. Cliff 02

Figure 209. Cliff 03

Figure 212. Cliff 04

Figure 207. Force field front view

Figure 210. Force field front view

Figure 213. Force field front view

Figure 208. Force field left view

Figure 211. Force field left view

Figure 214. Force field left view

Figure 215. Cliff 05

Figure 218. Cliff 06

Figure 221. Cliff 07

Figure 216. Force field front view

Figure 219. Force field front view

Figure 222. Force field front view

Figure 217. Force field left view

Figure 220. Force field left view

Figure 223. Force field left view

Figure 224. Cliff 08

Figure 227. Cliff 09

Figure 230. Cliff 10

Figure 225. Force field front view

Figure 228. Force field front view

Figure 231. Force field front view

Figure 226. Force field left view

Figure 229. Force field left view

Figure 232. Force field left view

Generative system

Cellular Automata

Figure 233. Nine states

Figure 234. Copying the state of the voxels that the projection of the force points

Figure 235. Changing the pattern to the next layer

Figure 236. Following the state of neighbors within visibility range

Figure 237. Transform into state = 0 because of overpopulation

Figure 238. Born because of empty local range

Considering the importance of local interactions, environmental feedback and information transfer for the adaptability of a system, we focus once more on the “Data Field” project, analyzing how the receiving information and its dynamic translation in space becomes the canvas for the generation of an adaptive system that can produce a structure that climbs up the landscape. Having diffused the data into the three-dimensional space, we use cellular automata, as a computational method to initialize the first connections of the generative system. Cellular automata have been tested many times in the architectural field as a form-finding tool that can lead to unexpected results. Implemented in one or two or even three-dimensions, cellular automata can be used as a system of spatial architectural explorations. Although the results are of high complexity the system itself is very simple. Dividing a level or a threedimensional space into cells and assigning them several different states the initial condition of the system is set (usually two states corresponding to dead or alive). Then, based on simple rules of death, birth and survival that depend upon the local interrelations of the cells the initial states change and the system seems to tempo-spatially evolve as cells might die, stay unchanged, or even reborn in different time sequences. Cellular Automata have the ability to generate highlevel complexity and variability over time through simple local rules. Therefore, cellular automata applications approach natural phenomena processes and expressions. At the same time through the alternation of states of the cells themselves but also of the total configuration over time, cellular automata seem to be an architectural tool of defining emptiness, space and form. In ‘Data - tectonics’ project cellular automata, as a generative tool contributes to the fulfilment of the design purposes and objectives. At first, cellular automata are used as a means to configure the identity of the space in-between the landscape and the upper surfaces by defining structural, inactive and architectural, active spaces. The goal of the system is to generate structures ,configuring at the same time spaces walked by and habited by people. Cellular automata serve this spacedefining generative scope. At the same time cellular automata can easily involve and make use of the directionality of the forcefield into the generative design process. As the first step, we explore the potential of this method at a two-dimensional level. Each cell of the level hosts a fixed vector assigned to it by the forcefield. As the goal is to interconnect the cells to create the lines of the structure, we assign randomly 9 possible states to the cells that correspond to possible connections of the cells. state 0 does not generate any connection and states from 1-9 correspond to connections of different directionality. At the second step, each cell checks the directionality of the vector that it hosts. The area that the vector points becomes the neighboring local area, with which it will interact and change its state. Focusing on the area that the vector points, the cell reads the states of its neighbors in a changeable range and adapts the average state of the neighboring area. This is the basic rule that follows the directionality imposed by the forcefield, drives the system and defines the structural patterns. Besides the basic rule, we define two more rules for the configuration of space. Firstly, we generate state 0 cells (no connection) in areas where there is high concentration of active states (connections). Secondly, we generate active states (more connections) in areas of low connectivity (0 state).

Figure 239. Layers of the 2D cellular automata’s result

Figure 240. Fifty frames of the 2D cellular automataâ&#x20AC;&#x2122;s result

Generative system

The steps of the generative system

Overview of the three generations

After testing the architectural patterns in the two-dimensional space, we translate the process to the three-dimensional space. We assign to the voxels of the upper level of the area of intervention randomly the 9 possible different states and then we iterate the two-dimensional process but this time copying each time the result to the layer below. As the states of the voxels change through the local interactions, they realize the main connections of the structure and copy their state to the voxels below. The process continuous till we reach the lowest part of the landscape below. This way we translate a process that evolves over time into spatial evolution as well. To better understand the generative system and how it develops we will now focus on the different steps of the process and the different generations of the system. Firstly, the main horizontal connections of the system get generated from top to bottom, driven by the cellular automata rules and the directionality of the forcefield. Connections of vertical direction get generated from top to bottom as well to complete the first low-resolution main connections of the structure. These lines correspond to the elements of the primary structure. When this step is complete, new information is extracted from the system concerning the first generation of lines. Based on these information, a second set of horizontal and vertical connections gets generated reinforcing the primary structure. This also applies for the third generation of lines too. Three generations of connections are created in total, with a logic that resembles that of fractals. The first concerns the main horizontal and vertical connections of the structure, the second the higher-resolution horizontal and vertical reinforcement of the first, and the third an even higherresolution reinforcement of the previous ones. Below we can see more clearly the steps of the process for each generation.

Figure 241. Forcefield of different cliffs

Figure 242. First generation

Figure 243. First and second generation

Figure 244. First, second and third generation

Generative system

The steps of the generative system

First generation

The fist main horizontal connections get initialized by cellular automata rules and the directionality of the forcefield on the upper level of the area of intervention. As the cellular automata run in the informationfilled space from top to bottom, the main horizontal connections of different directionalities form in the space. These constitute the main horizontal structure and at the same time define the structural and walkable, habitable areas of the space.

Figure 245. Logic of horizontal connections

Figure 246. Logic of vertical connections

Based on the information extracted from the main horizontal structure, vertical lines start forming, connecting the horizontal layers inbetween them and completing structurally the first low resolution generation. The vertical connections also start from top to bottom. They get initialized on the voxels of the upper level that already have a horizontal connection and move layer by layer till they reach the bottom. At each level, they check the 5 neighboring voxels of the level below in x and y directions that form a cross to decide where to connect. The decision of connection is made based on a minimum and maximum number of connections that has been set for each point of the structure. The connections on the perpendicular, have a higher probability of forming due to stability reasons.

Figure 247. Horizontal connections

Figure 248. Vertical connections

Figure 249. Horizontal and vertical connections

Generative system

The steps of the generative system

III

Next generations

Figure 250. Voxels of first generation

Figure 253. Rotations of next generations

Figure 257. First generation

Figure 251. Voxels of second generation

Figure 254. Three generations, movements of voxels

Figure 258. Second generation

Figure 255. First generation

Figure 259. Third generation

Figure 252. Voxels of third generation

Figure 255. Second generation

Figure 256. Third generation

Figure 260. Three generations

Figure 261. First generation

Figure 264. First generation

Figure 262. Second generation

Figure 265. Second generation

Figure 263. Third generation

Figure 266. Third generation

After the main structure has formed, higher resolution connections get generated in specific areas of the space where is needed. All the voxels in space store the information concerning their distance to the bottom landscape. At the same time the voxels store the information of the kind of space that was generated from the main structure knowing whether they are part of the structural or walkable or habitable space. Two ranges of distances are defined one for the second generation of connections and a smaller one for the third generation. Based on this information the voxels that lie in a specific distance from the cliff and the voxels that support habitable and walkable spaces get activated for the higher resolution connections. 2nd generation The activated voxels in range generate new horizontal and vertical connections of higher resolution that complement the main structure.

3rd generation The activated voxels in range generate new horizontal and vertical connections of higher resolution that complement the main and secondary structure. Summarizing, â&#x20AC;&#x2DC;Data Fieldâ&#x20AC;&#x2122; project attempts to design a system that can generate architectural structures over rocky landscapes, capable of adapting to a variety of given landscapes. This system is based on a data analysis, data processing and information transfer strategy. It gets initialized by the data of the environment, it develops based on the information it encounters in the space in-between and readjusts its configuration based on the receiving information from the system itself. This way a system is created that can perform in various contexts, different scales, different resolutions even different selection of data that drive the system. Below we can see some examples of how the system adapts in different contexts.

Generative system

The steps of the generative system

Overview of horizontal and vertical connections

Figure 267. The three horizontal generations

Figure 268. The three horizontal generations, top view

Figure 269. The three vertical generations

Figure 271. The three vertical generations

Figure 270. The three vertical generations

Figure 272. The three horizontal and vertical generations

Generative system

Process breakdown

Figure 273. Top limits

Figure 277. Second pattern

Figure 281. Sixth pattern

Figure 285. Vertical patterns

Figure 274. Force field

Figure 278. Third pattern

Figure 282. Seventh pattern

Figure 286. Walkable spaces

Figure 275. First generation’s voxels

Figure 279. Forth pattern

Figure 283. Eighth pattern

Figure 287. Second generation’s voxels

Figure 276. First pattern

Figure 280. Fifth pattern

Figure 284. Ninth pattern

Figure 288. Second generation’s connections

Figure 289. First and second generation

Figure 293. Vertical connections

Figure 295. Second generation (color coding)

Figure 297. All generations (color coding)

Figure 290. Third generationâ&#x20AC;&#x2122;s voxels

Figure 294. First generation (color coding)

Figure 296. Third generation (color coding)

Figure 298. All generations

Figure 291. All the connections

Figure 292. Horizontal connections

Figure 299. Habitable spaces

Optimization

â&#x20AC;&#x2DC;Data Fieldâ&#x20AC;&#x2122; project makes use of the data not only to initialize and generate the system but also to evaluate and optimize its results. Each output of the system depends on the values of the input parameters that configure its development and evolution. The input of the system consists of the parameters that define the final configuration of the forcefield and the parameters that control the range of cellular automata rules. The parameters of the forcefield are of two different types. The first type consists of the three parameters of weight, that control the magnitude of each data effect to the forcefield. The second type is the three parameters of range that control the field of influence of each data effect to the forcefield. These parameters define the final directionality that will drive the system. The parameters of the cellular automata rules, define the range of the local neighborhoods, based on which each voxel reconfigures its state. There are three range parameters, one for each rule. By testing different values of the parameters that form the input of the system, we can have different variations of the system, the output. We set specific criteria for the evaluation of the output and we extract the corresponding information from the system. We calculate the total structural length, the percentage of walkable space of the structure and the percentage of habitable space of the structure. The goal is to minimize the length of the structure, so we set its maximum value to 0, maximize the architectural spaces intended for walkable and habitable areas, so we set these maximum values to 1. We assign different weights to each one of these criteria, according to the design objectives and we calculate a final total score for each output. The ideal, non-realistic result would be 1.

SCORE = 0.5 * Walkable Space + 0.3 * Habitable Space+ 0.2 * Total Structural Length We test different values for the parameters of the system, and calculate the corresponding scores. We run several simulations changing the values of one parameter at a time, keeping the other stable. This way we can explore which range of values or which combination of parameters can provide us the output with the highest score, the one that performs best. In the following catalogues you can see the different results, structures, spatial configurations, shapes and scores we can have when we test the input values of the system. Although these tests now are conducted by the designer, the next step is to automate the process. By using machine learning techniques, we can automate the process of input and output, run millions of tests and create our own database of parameters and scores. Through this database we can search for the structure that best meets our criteria. This process allows the designer to objectively decide and keep what is more efficient. At the same time, we can explore which set of parameters, which combination of data provides us the best results and we can try to decode the underlying order and patterns of data and information that can give us the optimized performance of the system. As we can see input data directs the system but also output data feeds back to the system to identify, which are those elements among thousands that perform the best.

Optimization

Input and output

Input

Cellular Automata

Range of activated neighbors

Force field

Weight map and ranges

Follow the state of neighbors within visibility range

Height

Transform into state = 0 because of overpopulation or loneliness

Normal vectors

Born because of empty local range

Direction of slope

Output

Structural length

Walkable space

Habitable space

Optimization

Cellular Automata

Overview

Optimization

Cellular Automata

Tests

Figure 300. Perspective view

Figure 301. Force field

Figure 302. Color coding

Figure 303. Perspective view

Figure 304. Force field

Figure 305. Color coding

Figure 306. Perspective view

Figure 307. Force field

Figure 308. Color coding

Figure 309. Perspective view

Figure 310. Force field

Figure 311. Color coding

Figure 312. Perspective view

Figure 313. Force field

Figure 314. Color coding

Figure 315. Perspective view

Figure 316. Force field

Figure 317. Color coding

Figure 318. Perspective view

Figure 319. Force field

Figure 320. Color coding

Figure 321. Perspective view

Figure 322. Force field

Figure 323. Color coding

Figure 324. Perspective view

Figure 325. Force field

Figure 326. Color coding

Figure 327. Perspective view

Figure 328. Force field

Figure 329. Color coding

Figure 330. Perspective view

Figure 331. Force field

Figure 332. Color coding

Figure 333. Perspective view

Figure 334. Force field

Figure 335. Color coding

Optimization

Force Field wIth different ranges

Overview

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Optimization

Force Field wIth different ranges

Tests

Figure 336. Perspective view

Figure 337. Force field

Figure 338. Color coding

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Figure 339. Perspective view

Figure 340. Force field

Figure 341. Color coding

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Figure 342. Perspective view

Figure 343. Force field

Figure 344. Color coding

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Figure 345. Perspective view

Figure 346. Force field

Figure 347. Color coding

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Figure 348. Perspective view

Figure 349. Force field

Figure 350. Color coding

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Optimization

Force Field wIth different weight maps

Overview

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Optimization

Force Field wIth different weight maps

Tests

Figure 351. Perspective view

Figure 352. Force field

Figure 353. Color coding

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Figure 354. Perspective view

Figure 355. Force field

Figure 356. Color coding

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Figure 357. Perspective view

Figure 358. Force field

Figure 359. Color coding

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Figure 360. Perspective view

Figure 361. Force field

Figure 362. Color coding

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Figure 363. Perspective view

Figure 364. Force field

Figure 365. Color coding

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Adaptability

Different top limits

Overview

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Adaptability

Different top limits

Tests

Figure 366. Perspective view

Figure 367. Force field

Figure 368. Color coding

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Figure 369. Perspective view

Figure 370. Force field

Figure 371. Color coding

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Figure 372. Perspective view

Figure 373. Force field

Figure 374. Color coding

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Figure 375. Perspective view

Figure 376. Force field

Figure 377. Color coding

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Figure 378. Perspective view

Figure 379. Force field

Figure 380. Color coding

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Adaptability

Different cliffs

Overview

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Adaptability

Different cliffs

Tests

Figure 381. Perspective view

Figure 382. Isometric view of the cliff

Figure 383. Force field

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Figure 384. Perspective view

Figure 385 Isometric view of the cliff

Figure 386. Force field

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Figure 387. Perspective view

Figure 388. Isometric view of the cliff

Figure 389. Force field

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Figure 390. Perspective view

Figure 391. Isometric view of the cliff

Figure 392. Force field

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Figure 393. Perspective view

Figure 394. Isometric view of the cliff

Figure 395. Force field

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Figure 396. Perspective view

Figure 397 Isometric view of the cliff

Figure 398. Force field

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Figure 399. Perspective view

Figure 400 Isometric view of the cliff

Figure 401. Force field

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Figure 402. Perspective view

Figure 403. Isometric view of the cliff

Figure 404. Force field

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Figure 405. Perspective view

Figure 406. Isometric view of the cliff

Figure 407. Force field

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Figure 408. Perspective view

Figure 409. Isometric view of the cliff

Figure 410. Force field

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Fabrication

Materials and geometry

The physical model is a one to one scale structure using circular profiles steel with two-pieces flexible joints, as shown in Figure 415. Firstly, the round tube with joints could be suitable for the flexible frame structure. Its inspiration is scaffolding. Secondly, two-pieces joints are easier for workers to install it after placing the steel tubes without moving anything. Thirdly, flexible and standard joints can use in all connection parts, so it is a way to achieve the autonomy of system and simplify the indistinguishable fixed joints. It is essential to highlight that the system is influenced by fabrication and limit the structures in x-y, x-z and y-z plane because of the limitation of joints in vertical connections.

Figure 411. Horizontal layers

Figure 412. Vertical layer, x parallel

Figure 413. Vertical layer, y parallel

Figure 414. Combination of layers

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Figure 415. Two-pieces Flexible Joints

145

Fabrication

Assembly line

Camera sensor and non label material

The digital test steps of assembling the structure need to explain more. The first thing is to export the digital models as a CSV file that can import into the grasshopper. The CSV file includes six numbers that show the x,y,z coordinate of the start point and end point, respectively. The two points can create a line with a certain length. Then these lines from the digital model will be mapped and arrayed in the frame. The tool path is connections from the middle point of the sticks on the frame to the target points in the assembly area. In the process of the robot arm assembly test, there are some technical issues need to be solved. For instance, the robot arm sometimes may not place the stick in a right position in the real test, because of the high accuracy requirements of hardware and position. One possible reason is that the connector between the gripper and the robot arm is aging and deformation. Another reason is that the distance between the real frame and assembly area is different from the distance in the digital simulation. In this case, the steel tubes are placed in a fixed area with a specific order the same as it does in the digital simulation. It is difficult to handle a large number of steel tubes with an order, despite using a label to distinguish them. Hence, Kinect, a sensor can recognise RGB colour and depth by one camera, an infrared laser projector and a monochrome CMOS sensor is a possible solution to deal with a vast number of non-label materials and experimental errors. Specifically, Microsoftâ&#x20AC;&#x2122;s Kinect sensors are utilised as an input device and using depth and colour information to segment the steel tubes from the background. The algorithm for Kinect computer vision is blob analysis. There are four steps to achieve the goal that check the tubesâ&#x20AC;&#x2122; length and choose the correct length from many non-label materials. The images of non-label material are divided into pixels to segment the background pixels with target pixels as the first step. The second step is to connect the single target pixels. Then the morphological parameters, such as the length of the steel tubes of the target object are measured.

Figure 416. Angles and standard lengths

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Figure 417. Blob algorithm diagram

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Fabrication

Assembly line

Overview of the on site assembly

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Fabrication

Assembly line

III

Step 01

We mark the center of each voxel and all the needed space for the joints We put a dis-assembled wooden grid that provides all the possible positions for the pipes

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Fabrication

Assembly line

Step 02

Pipes are placed in order, so the robotic arm collect one by one and place them to the correct horizontal position Next, workers conclude the semi-autonomous assembly by connecting the pipes with a variety of joints A horizontal layer has been composed

150

Fabrication

Assembly line

Step 03

Pipes are placed in order, so the robotic arm collect one by one and place them to the correct vertical position

The grid is transported to a couple of linear levels that supports it and at the same time allows it to connect to another profile that it stands in a lower level

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Fabrication

Assembly line

Step 04

The two profiles are jointed by the workers. At the same time they dis-assembly the wooden grids. The . profile is ready to be transported on to the cranes

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Fabrication

Assembly line

Step 05

Cranes transport the patterns on to the rock for the final step of the assembly.

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Fabrication

In the laboratory

Gripper and claw preparation Camera sensor: scan and check length Horizontal assembly Vertical assembly

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Proposal for the B-Pro Show

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Experimentations on physical models

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Applications

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