Advanced Design & Digital Architecture by diego suarez

Advanced Design & Digital Architecture

Diego A. Suรกrez Traverso

ADDA Advanced Design & Digital Architecture

Diego A. SuรกrezBarcelona Traverso 2011 . 2012

Diego A. Suรกrez Traverso

Arch. UCB, Master Advance Design and Digital Architecture - Elisava - Universitat Pompeu Fabra

Team Members: Alba Armengol Gasull Arch. UIC, Master Advance Design and Digital Architecture - Elisava Bart Chompff Amoud B.Sc. Arch. TU Eindhoven, Master Advance Design and Digital Architecture - Elisava

Professors:

Lectures:

Jordi Truco - Director ADDA Arch. ETSAB, MArch Emtech AA

Michael Weinstock - Director of Architectural Association School of Architecture.

Roger Paez - Professor Arch. ETSAB, MArch GSAPP Columbia

Neil Leach - Professor of University of Southern California

Pau de Sola Morales - Professor Arch. ETSAB, Phdw. Harvard

Marta Male - Co-Director at Institute for Advanced Architecture of Catalonia.

Marcel Bilurbina - Professor Arch. ETSAB, Master Arts Digitals Pompeu Fabra

Freddy Massad -Professor of Escola Superior de Disseny i Enginyeria de Barcelona

Fernando de Lecea - Professor Arch. ETSAUN, Master Advance Design and Digital Architecture - Elisava Marco Verde - Professor MArch Architecture Biodigital, Esarq. UIC David Lorente - Professor Graphic Desginer, ACTAR-Birkhauser

Bio Design Laboratory

C O N TEN TS

01. Course Introduction 02. Case Study “Beijing Olympic Stadium” 03. Essay “Synchronization and Emergence System” 04. Introduction 05. Site 06. Component Definition 07. Algorithmic Proliferation 08. Re-Configuration Component 09. Algorithmic Proliferation 10. Digital Tectonics 11. Control Engineering 12. Mechacronics 13. Architectonic Application 14. Fabrication 15. Installation Elisava’s hall

09 11 21 47 49 61 71 101 103 111 119 131 139 167 208

Computational Design Laboratory 01. Introduction 02. Genetic Vs. Generative 03. Data Collection & Site Study 04. Operative Strategies 05. Animated Scenario 06. Intelligent Patterns 07. Digital Morphogenesis 08. Prototype 09. Architecture Response 10. Bibliography

225 227 237 249 265 277 307 323 337 354

Bio Design Laboratory

01 _

Introduction

There is growing interest in finding guidelines in living systems to help us understand new forms of designing. On occasion, this interest makes the mistake of wishing to imbue designs with a veneer of new organic ways, imitating natural forms, perhaps unconsciously aided by the incredible digital modeling resources we are increasingly able to master. This could not be further from our intentions at the Bio Design laboratory (ADDA). We focus our interest on observing how biological organisms achieve complex emergent structures from simple components. The structures and forms generated by natural systems are analyzed and understood as hierarchical organization of very simple components (from the smallest to the largest), in which the properties arising in an emergent manner are rather more than the sum of the parts. In our constantly developing society, with its demanding market, the use of new production technologies in fields such as engineering is becoming more frequent, and research is conducted to create state of the art materials, such as composites, which open up new possibilities of use and performance, and contain the logic of living materials. In the field of architecture, even more rightly, we are forced to regain this sensitivity in observation and research, and learn the lesson of nature on the act of formalizing and metabolizing. Our objective is to learn and explore this knowledge to then transfer it and apply to the design process of architecture and spaces. - Jordi Truco (1), course introduction. (1) Jordi Truco, Arch. ETSAB, MArch Emtech AA. Partner of HYBRIDa Studio and director of the Advanced Design and Digital Architecture master programme at ELISAVA, Barcelona.

Introduction

02 _

Case study “Beijing Olympic Stadium”

The Beijing National Stadium is a defining piece of architecture for 21st Century China. The Olympics have ushered in a new era in Chinese construction history and nothing symbolizes this better than the “Bird’s Nest”, with its dramatic visual impact and stylistic cues that blend modern steel construction with forms found in nature. The stadium is unique for its look as well for its operating systems and construction; part of the reason it is such an intriguing building. The core building systems had to perfectly match the design symmetry and visual look of the stadium, while providing high quality, efficient operations to match the Beijing Olympics’ environmentally-friendly theme. This bold, innovative design successfully combines aspects from China’s past and present, this is why it is called “culture-defining landmark”.

Case Study

Location: Beijing China Type: Sport Facility Size: 80,000 seat arena Team: Herzong & de Meuron, Arup Sport, China Architecture Design and Research Group Duration: 2003-2004 Herzog & de Meuron Team: Partners: Jacques Herzog, Pierre de Meuron, Stefan Marbach

12 Case Study

Description

Outcomes

The â&#x20AC;&#x153;birdâ&#x20AC;&#x2122;s nestâ&#x20AC;? beijing Olympic Stadium is well known for its unique design of interwoven curved structural steel trusses. The design and engineering was achieved through a close collaboration between the architectural firm Herzog & de Meuron, and the stadium engineering team at Arup Sport. GT worked with the architect and engineer to coordinate the development of a parametric model of the stadium and structural systems. This process included reverse engineering the structural and architectural design intent, then encoded this logic parametrically to allow iterative dimensional modifications on a highly detailed overall project model. Catalogs of intelligent, reconfigurable structural elements were developed that were instantiated into the parametric design part to complete the detailed design. During construction, the model was used to extract dimensions for the development of steel fabrication packages by local steel fabricators in China.

The parametric modeling approach allowed rapid development of steel design early in design development and allowed this information to be automatically updated over design changes. Late in design development, significant value engineering changes were initiated, including the removal of a previously designed retractable roof. Working with GT and the parametric modeling approach, the design, model and drawings were revised to reflect the new strategy in less than three weeks. The parametric modeling approach also subs tially assisted in the resolving the complex geometry of other key design elements including the curved exit stairs that course between the exterior truss system and the interior surfaces. Highlights Parametric models of the steel system and stadium seating were used for rapid design modifications. Models served to accelerate value engineering changes, reducing steel weight by 30% Models were used as the basis for producing steel shop drawings GT worked between design offices across Europe and the Chinese based design institute and construction team.

Case Study

The stadium looks like a gigantic collective shape, like a vessel whose undulating rim echoes the rising and falling ramps for spectators inside the stadium. From this distant perspective, one can clearly distinguish not only the rounded shape of the building but also the grid of the load-bearing structure, which encases the building, but also appears to penetrate it. What is seen from afar as a geometrically clear-cut and rational overall configuration of lines, evaporates the closer one comes, finally separating into huge separate components. The components look like a chaotic thicket of supports, beams and stairs, almost like an artificial forest.

14 Case Study

The plinth The geometries of the plinth and stadium merge into one element, like a tree and its roots. Pedestrians flow on a lattice of smooth slate walkways that extend from the structure of the stadium. The spaces between walkways provide amenities for the stadium visitor: sunken gardens, stone squares, bamboo groves, mineral hill landscapes, and openings into the plinth itself. Gently, almost imperceptibly, the ground of the city rises and forms a plinth for the stadium. The entrance to the stadium is therefore slightly raised, providing a panorama of the entire Olympic complex.

Structure = façade = roof = space

The bowl

The spatial effect of the stadium is novel and radical, and yet simple and of an almost archaic immediacy. Its appearance is pure structure. Façade and structure are identical. The structural elements mutually support each other and converge into a spatial grid-like formation, in which façades, stairs, bowl structure and roof are integrated. To make the roof weatherproof, the spaces in the structure of the stadium are filled with a translucent membrane, just as birds stuff the spaces between the woven twigs of their nests with soft filler. Since all of the facilities – restaurants, suites, shops and restrooms – are self-contained units, it is largely possible to do without a solid, enclosed façade. This allows natural ventilation of the stadium, which is the most important aspect of the stadium’s sustainable design.

The stadium is conceived as a large collective vessel, which makes a distinctive and unmistakable impression both when it is seen from a distance and from close up. Inside the stadium, an evenly constructed bowllike shape serves to generate crowd excitement and drive athletes to outstanding performances. To create a smooth and homogeneous appearance, the stands have minimal interruption and the acoustic ceiling hides the structure in order to focus attention on the spectators and the events on the field. The human crowd forms the architecture. Herzog & de Meuron, 2007

Case Study

Structure:

Structural Design

The primary structure of the roof is independent of the bowl structure and is conceived as a series of steel space frames wrapped around the bowl. The outer facade is inclined at approx. 13 degrees to the vertical. The overall depth of the structure is 12 m. The total steel tonnage is 41,875 t; 36km of unwrapped steel length.

24 Primary trusses at regular intervals around the base geometry The angle 13 degrees up to the middle, giving the building its saddle shape form. Secondary, Tertiary steel members are laid out between the primary members, who are equally and regular divided.

The roof is saddle-shaped. Spaces between the steel members are filled with ETFE foil (38â&#x20AC;&#x2122;000 sqm single stressed foil) on the upper surface and an acoustic membrane (PTFE fabric) on the lower surface; this second layer reflects and absorbs sound to maintain the atmosphere in the stadium. The bowl superstructure consists of in-situ concrete. Priority for the design of the seating bowl was to get spectators as close as possible to the action with clear sight lines. Ventilation: The stadium bowl is naturally ventilated.

16 Case Study

The turrets receive two convergent beams at the same time, which compensate its efforts. Considering the secondary structure for the circulation with diagonals pieces of similar dimensions and finishing, suggesting a homogeneous composition. The secondary structure seems random, but by using digital design and analyzing software they were calculated for structural needs.

Digital Design and Analysis Why does a Chinese bowl or a Chinese window have this kind of pattern? Maybe the Chinese people like things to appear in this irregular way, but underneath there are very clear rules. The Bird’s Nest develop ed in this way The tridimensional structure design was elaborated with CATIA software and analyzed with ANSYS The load capacity and connections were substantially improved through the structural analysis software with a readjustment of the main and secondary structure.

Advanced computer analysis and modeling has ensured that the structure has the ability to withstand major earthquakes. The same software was able to reduce the estimated steel to 50% steel consume at first by 80 000 ton to a slightly more than 40 000 ton, composed by 36 km of pieces.

Case Study

Rapid Prototyping Rapid prototyping can be defined as a group of techniques used to quickly fabricate a scale model of a physical part or assembly using three-dimensional computer aided design (CAD) data. Construction of the part or assembly is usually done using 3D printing technology. The first techniques for rapid prototyping became available in the late 1980s and were used to produce models and prototype parts. Today, they are used for a much wider range of applications and are even used to manufacture production-quality parts in relatively small numbers. Some sculptors use the technology to produce exhibitions.

18 Case Study

The design of the structural elements required a detailed tridimensional modeling. A particularly important component was the junction between the pyramidal turrets and the beams that had a similar shape but different traces each element, besides being a critical element to the assembly. For this reason, several turrets models were generated, through rapid prototyping (stereo lithography), mainly to ensure the combination of structural and aesthetic requirements.

Generative Components is associative and parametric modeling software that can be used by architects and engineers to automate design processes and accelerate design iterations. It gives designers and engineers new ways to explore alternative building forms without manually building a detailed design model for each scenario. It also increases efficiency in managing conventional design and documentation. â&#x20AC;&#x153;Generative design is not about designing a building, Itâ&#x20AC;&#x2122;s about designing the system that designs a buildingâ&#x20AC;?.

Case Study

03 _

Synchronization and Emergence System Synchronization is the experience of two or more events that are apparently causally unrelated or unlikely to occur together by chance and that are observed to occur together in a meaningful manner. The emergence of order by two seemly separating events is what we call synchronicity. - Steven Strogatz in “The emerging science of spontaneous order SYNC”

Emergence is what happens when a system of relatively simple elements organize spontaneously and without explicit laws to give rise to intelligent behaviour. Term to describe the bottom-up or self-organization in many fields. - Steven Johnson in “Emergence”

Synchronization and Emergence System

Synchronization “FireFly”

“ A great belt of light, some ten feet wide, formed by thousands upon thousands of fireflies, whose green phosphorescence bridges the shoulder-high grass. The fluorescent band composed of these tiny organisms lights up and goes out with a precision that is perfectly in sync. Its like they have secret mechanical devices to communicate their shining,” Joy Adamson. (1)

“ Some twenty years ago I saw, or thought I saw, a synchronal or simultaneous flashing of fireflies. I could hardly believe me eyes, for such a thing to occur among insects is certainly contrary to all natural laws !” Philip Laurent. (2)

(1) Joy Adamson, (20 January 1910 – 3 January 1980) (born Friederike Victoria Gessner) was a naturalist, artist, and author best known for her book, Born Free, which

describes her experiences raising a lion cub named Elsa. Born Free was printed in several languages, and made into an Academy Award-winning movie of the same name. (2) Philip Laurent, “The supposed synchronal flashing of fireflies,” Science 45 (1917), page 44.

22 Synchronization and Emergence System

Individual - Local Behaviour - Each firefly in a swarm has an oscillator that fire repetitively out of sync - It generates a rhythm of bioelectrical current that travels to the fireflyâ&#x20AC;&#x2122;s lantern - At the lantern it creates a bio-luminent green flash as the result of a chemical reaction that is trigger.

Firefly

Group - Global Behaviour - Fireflies all poses an oscillator that communicate with each other - The impulses of light each oscillator gets triggers - Result of the conversation is the emergence of synchronicity

Firefly Synchronization and Emergence System

Firefly A

24 Synchronization and Emergence System

D=X

D<X

Firefly B

Group - Global Behaviour - If firefly A flashes and resets to 0 AND firefly B picks registers the flash and gets overcooked than they both fire together and wonâ&#x20AC;&#x2122;t get out of sync anymore.

- When distance is smaller than X firefly A add impulse to recharger of B

-Result is a self organizing / self synchronizing system

- After flash charger is reset to 0

- Recharger B gets over clocked and is reset to 0

Synchronization and Emergence System

26 Synchronization and Emergence System

Photograph by Digital Photo Blog

Flashed are adjusted to each other by oscillators

Conditions for sync in fireflies

Note

- A affects B and create an inseparable unity. - AB affects C and creates an new inseparable unity.

- Oscillators must be similar enough.

- Fireflies do not have a hierarchical structure.

- Oscillators must allow communication. - Distance must be small enough.

- There is no leader involved who initiates the flashing.

- ABC affects D, etc. - No outside instructions. - Flashes are used to attract femaleness from large distances. Synchronization and Emergence System

Synchronization “Human heart”

- When voltage reaches a threshold, the capacitor discharges, and voltage drops to zero. - Peskin (1) discovered that our pacemakers are collections of thousands of oscillators. - A simple model for synchronous firing of biological oscillators based on Peskin’s model of the cardiac pace maker. - The model consists of a population of identical integrate-and-fire oscillators. The coupling between oscillators is pulsate: when a given oscillator fires, it pulls the others up by a fixed amount, or brings them to the firing threshold, whichever is less.

(1) Peskin, (20 January 1910 – 3 January 1980) he born Friederike Victoria Gessner he was a naturalist, artist, and author best known for her book, Born Free, which describes her experiences raising a lion cub named Elsa. Born Free was printed in several languages, and made into an Academy Award-winning movie of the same name.

28 Synchronization and Emergence System

Human hearth

Hearth beats

Synchronization and Emergence System

Cell A

30 Synchronization and Emergence System

D=X

D<X

Cell B

Group - Global Behaviour - When distance is smaller than X cell A add impulse to recharger of B - Recharger B gets over clocked and is reset to 0

- If cell A pulses and resets to 0 and cell B picks registers the flash and gets over clocked than they both fire together and wont get out of sync anymore.

- After pulse charger is reset to 0

- Redundant system

Synchronization and Emergence System

Sync - Start First cluster

Sync - First cluster grows / Second cluster begins

Sync - First cluster grows / Second cluster grows

Sync - Full synchronization

32 Synchronization and Emergence System

Synchronization “Cybernetics”

Norber Wiener (1) wrote his classic “cybernetics” in 1950 we was PHD from Harvard at age 18 and central figure in science of synchronization The field of cybernetics came into being in the late 1940’s when concepts of information, feedback, and regulation [Wiener 1948] were generalized from specific applications in engineering to systems in general, including systems of living organisms, abstract intelligent processes, and language. Cybernetics: “a Greek word meaning “the art of steering” to evoke the rich interaction of goals, predictions, actions, feedback, and response in systems of all kinds”

(1) Norber Wiener,(November 26, 1894, Columbia, Missouri – March 18, 1964, Stockholm, Sweden) was an American mathematician. He was Professor of Mathematics at MIT. Wiener is regarded as the originator of cybernetics, a formalization of the notion of feedback, with many implications for engineering, systems control, computer science, biology, philosophy, and the organization of society.

Synchronization and Emergence System

Wiener posed that sync was everywhere

Millions of oscillators working in the brain ( neurons )

- Chirping crickets

Diverse oscillators for different functions

- Croaking frogs

Some fire 12 per second, some 8 times per second, others 6.

- Flashing fireflies

To work together they need to sense each others rhythm.

- Rotating asteroids - Power grids

Each neuron or cluster of neurons pulls at the others to increase or decrease the frequency and therefore creates selforganization.

- Brain waves

This is called FREQUENCY PULLING

34 Synchronization and Emergence System

Frequency pulling

Frequency pulling creates spontaneous synchronization in populations - Oscillators have to be able to communicate - Oscillators have to have to be in the same frequency range Oscillators freeze into sync, not in space, but in time. If the difference between oscillators is to big they are not able to pull each other in. Conclusion: populations of fireflies, brain waves, heart cells, etc have to be similar enough or they wonâ&#x20AC;&#x2122;t be able to recognize each otherâ&#x20AC;&#x2122;s frequency.

Synchronization and Emergence System

Emergence “The Connected Lives of Ants, Brains, Cities, and Software” Steven Johnson (1) wrote Emergence “The Connected Lives of Ants, Brains, Cities, and Software”: a term to describe complexity resulting from bottom-up or selforganization used in many fields, from the natural sciences to computer science to economics. The fact that agents whether they be ants, neurons, human individuals, or agents of another sort-- base their behaviour on their local environments without requiring knowledge about the system as a whole. Emergence is what happens when an interconnected system of relatively simple elements self-organizes to form more intelligent, more adaptive higher-level behaviour. It’s a bottom-up model; rather than being engineered by a general or a master planner, emergence begins at the ground level. Systems that at first glance seem vastly different--ant colonies, human brains, cities, immune systems-all turn out to follow the rules of emergence. In each of these systems, agents residing on one scale start producing behaviour that lays a scale above them: ants create colonies, urbanites create neighbourhoods.

(1) Steven Berlin Johnson, (born June 6, 1968) is an American popular science author; he attended the prestigious St. Albans School as a youth. He completed his

undergraduate degree at Brown University, where he studied semiotics, a part of Brown’s modern culture and media department. He also has a graduate degree from Columbia University in English literature.

Synchronization and Emergence System

Emergence “The connected lives of ants”

Although an ant colony requires a queen for perpetuating the population, her role stops there. The queen, despite implications of the title, plays no role in orchestrating the behaviour of the colony. Instead the activities of the colony result from interactions between individual ants. Each ant lives by a simple set of rules that guide her behaviour. For instance, an ant’s decision to forage is dependent on the frequency of contact she has with other ants in her immediate surroundings, rather than any knowledge of what the colony as a whole is doing. The result is what an onlooker might observe as intentional behaviour or hierarchical organization. Many behaviours of the colony though, such as allocation of duties or strategic placement of middle and ant corpses, result from this kind of collective behaviour or “swarm intelligence”. Attempts to identify any such true queen, directing ant, or “pacemaker” element prove fruitless. The “organizing force” is then a decentralized one with no single agent being in charge.

38 Synchronization and Emergence System

In the natural world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find food (see Ant communication). Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. The more time it takes for an ant to travel down the path and back again, the more time the pheromones have to evaporate. A short path, by comparison, gets marched over more frequently, and thus the pheromone density becomes higher on shorter paths than longer ones. Pheromone evaporation also has the advantage of avoiding the convergence to a locally optimal solution. If there were no evaporation at all, the paths chosen by the first ants would tend to be excessively attractive to the following ones. In that case, the exploration of the solution space would be constrained. Thus, when one ant finds a good (i.e., short) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leads all the ants following a single path. The idea of the ant colony algorithm is to mimic this behaviour with â&#x20AC;&#x153;simulated antsâ&#x20AC;? walking around the graph representing the problem to solve.

The original idea comes from observing the exploitation of food resources among ants, in which antsâ&#x20AC;&#x2122; individually limited cognitive abilities have collectively been able to find the shortest path between a food source and the nest. 1. The first ant finds the food source (F), via any way (a), then returns to the nest (N), leaving behind a trail pheromone (b) 2. Ants indiscriminately follow four possible ways, but the strengthening of the runway makes it more attractive as the shortest route. 3. Ants take the shortest route; long portions of other ways lose their trail pheromones. Synchronization and Emergence System

Antâ&#x20AC;&#x2122;s Movement

Mideem workers only

Patrollers only

Foraggers only

Notice that individual ants seem to be moving more or less randomly around in their work space. Even more importantly though, if you watch, youâ&#x20AC;&#x2122;ll notice that individual ants can and do switch tasks.

40 Synchronization and Emergence System

In a series of experiments on a colony of ants with a choice between two unequal length paths leading to a source of food, biologists have observed that ants tended to use the shortest route. A model explaining this behaviour is as follows:

1. An ant (called “blitz”) runs more or less at random around the colony; 2. If it discovers a food source, it returns more or less directly to the nest, leaving in its path a trail of pheromone; 3. These pheromones are attractive, nearby ants will be inclined to follow, more or less directly, the track; 4. Returning to the colony, these ants will strengthen the route; 5. If there are two routes to reach the same food source then, in a given amount of time, the shorter one will be travelled by more ants than the long route; 6. The short route will be increasingly enhanced, and therefore become more attractive; 7. The long route will eventually disappear because pheromones are volatile; 8. Eventually, all the ants have determined and therefore “chosen” the shortest route.

Synchronization and Emergence System

Emergence “Swarm Intelligence”

Swarm intelligence is the collective behaviour of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. Swarm intelligence systems are typically made up of a population of simple agents or boils interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behaviour, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling. The application of swarm principles to robots is called swarm robotics, while ‘swarm intelligence’ refers to the more general set of algorithms. ‘Swarm prediction’ has been used in the context of forecasting problems.

42 Synchronization and Emergence System

Each individual acts like an individual not like a group Following a simple set of rules they create the behaviour of the group 1. Avoid bumping into other individuals. (Distance) 2. Move in the average direction that those closest to you are heading. (Direction) 3. Move toward the average position of those closest to you. (Dynamic) 4. Get out of the way when predators are coming. (Danger)

Gnus

Birds

Fireflies

Fish

People

Locust

Wasp

Ants

Bees Synchronization and Emergence System

Conclusion Synchronization - Synchronization appears in living and life less entities. - Synchronization appears in intelligent and non-intelligent beings. - For synchronization to occur the oscillators must be similar enough. - For synchronization to occur the oscillators must allow communication between each other. - There is no hierarchical structure or leader involved. Synchronization is based on closest neighbour principle. Synchronicity is the experience of two or more events that are apparently causally unrelated or unlikely to occur together by chance and that are observed to occur together in a meaningful manner. The emergence of order by two seemly separating events is what we call synchronicity.

Synchronization is a working principle behind Emergence

44 Synchronization and Emergence System

Conclusion Emergence - Emergence is based on bottom-up or selforganization. - There are now explicit laws that give rise to intelligent behaviour. - Emergence itself creates hierarchy. - Emergence gives rise to complex patterns based on simple interactions. - There are not outside instructions needed.

Emergence is a working principle behind Synchronization

Synchronization and Emergence System

04 _

Introduction

The target of the studio is to explore an integral design approach towards multiperfomative material system. The object of the studio is to develop parametrically defined material systems that are structure and skin at the same time. The development of these systems will originate from the definition of their simplest constituents integrating manufacturing constraints and assembly logics in parametric components. The initially simple components will be proliferated into larger more complex system with differential density and permeability. The exploration towards structure as a performative skin and skin as a differentiate structure is divided into phases of work. (Course syllabus page12)

Introduction

05 _

Site

Analyzing the human behaviour emerges in natural areas we create a material system. Human behaviour What kind of human behaviour emerges in natural areas? How do groups of people organize themselves in these areas? What kind of architectural system could support and adept to the behaviour that emerges? The Challenges Analyze green areas in Barcelona. Analyze human behaviour in natural areas. Adaptation, integration and responsibility with the system.

Site

Park Creueta Coll

Park Migdia

50 Site

Park Joan Maragal

Park Guel

Park Joan Brossa

Park Del Guinardo

Park Ciudadela

Barcelona - Spain GREEN AREAS

Site

52 Site

Park Ciudadela Passeig de LluĂs Companys, 08018 Barcelona, Spain

Site

People tracking in the park / 10:00 am - 10:10 am

10:00 am

10:03 am

10:05 am

10:07 am

10:09 am

10:11 am

- Analyze the people tracking in the park, we followed 25 people about 10 minutes each. - Limits, condition and perimeter of the site. - Adaptation, integration and human behaviour in the site. 54 Site

Top view / Routing 10:11 am

Site

Barcelona Green Areas

Key factors that determine the behaviour of humans in natural areas. - ROUTING - PROXIMITY - SOLAR RADIATION

Park Ciudadela

56 Site

Key factors that determine the behaviour of humans in natural areas. ROUTING / PROXIMITY / SOLAR RADIATION

Routing frame 1

Routing frame 3

Proximity 1st Neighbor

Proximity 2nd Neighbor

Solar radiation Fall

Solar radiation Spring Site

Key factors that determine the behaviour of humans in natural areas. ROUTING / PROXIMITY / SOLAR RADIATION

58 Site

Routing frame 7

Routing frame 10

Proximity 3rd Neighbor

Proximity All Neighbors

Solar radiation

Summer

Winter

Create a material system that has: - Ability to support and adapt to the natural behaviour of urban citizens in park areas - Ability to sense and react to different stimuli. - Ability to create architectural space - Ability to facilitate various site related activities - Ability to function in small or large proliferation

Park Ciudadela Site

06 _

Component Definition

We started working by experimenting and learning from forms and materials (paper, plastic, wood, and metal), applying a some form finding techniques. In this process we had two weeks workshop â&#x20AC;&#x153;Time Based formations through Material Intelligence â&#x20AC;&#x153;. In the workshop process first of all we understood the material behaviour, such as folding, weaving, catenaries, minimal nets, minimal surfaces, structures, material properties, porosity, and material deformation. Learning the material behaviour and forms complemented with parametric software and advanced modeling we are able to create a material system, will enable us to produce designs that are totally innovative in material, form and behaviour, but also able to adapt to their environment. The system generates form, and each form generated is different depending on the programming requirements it needs to respond to.

Component Definition

Top view

Side view

Front view

Back view

First try/outs of materials and forms. We started with a circular rubber band, change material and implement some designs of connection. This is the evolution and attempts we made to get the final component. 62 Component Definition

Drawings

Top view

Side view

Front view

Back view

Drawings

Component Definition

Component A

Component B

Component C

Component Definition

The component comes from the evolution and many attempts. The component definition consists of a piece (band) circular, the circular band was designed with four joins, the caps were designed to generate 4 movement inside and outside, the covers also were designed to control porosity, light and strength of the material.

64 Component Definition

Component D

Template

Stick

Cover Ring

Materials: Polypropylene 8mm Screws 2 mm Machine: Laser Machine

Polypropylene Model

Component Definition

101 11

In order to continue analyzing the component we measured each component with photographs (top, front, back and side view)

102

Component A

101

97 19

Component B

66 Component Definition

Component C

Component D

Component Definition

Overlapping components A-B-C-D

68 Component Definition

From the measurement data, we generated a digital model in order to work more precisely.

Component A

Component B

Component C

Component D

Component A

Component B

Component C

Component D

Component A

Component B

Component C

B Component D

Component A

Component B

Component C

Component D

Component Definition

07 _

Algorithmic Proliferation

Will focused on the rule based proliferation of the developed component. This proliferated component system should provide all relevant information for manufacturing and assembly; so that the construction of a large prototype can begin. It is necessary in this phase the elaboration of catalogues of diversity of connections, this will help on understand and control the capacities of the system to create spatial articulation and diversity on form. This relevant information will be carefully registered for its further utilization to build the component in parametric software. Rules of proliferation based on the developed component. Study types of connections between the developed components. Connection between 2, 3, 4, 5, and 9 components. We documented by photography and then pass the measurements to a parametric program.

Algorithmic Proliferation

Connection and behavior study on four components Component A Component A Component A Component A

A1 A1

A1 A2

A1 A3

Connection 1 Connection 2 Connection 3 Connection 4 72 Algorithmic Proliferation

Connection 1 Connection 2 Connection 3 Connection 4

Component A Component A Component A Component A

Connection 1 Connection 2 Connection 3 Connection 4

Component A Component A Component A Component A

Connection 1 Connection 2 Connection 3 Connection 4

Component A Component A Component A Component A

A4 A1

A1 A1

A4 A1

A1 A1

Algorithmic Proliferation

Connection and behavior study on four components Component B Component B Component B Component B

B1 B1

B1 B1 B2

B1 B3

Connection 1 Connection 2 Connection 3 Connection 4 74 Algorithmic Proliferation

Connection 1 Connection 2 Connection 3 Connection 4

Component B Component B Component B Component B

Connection 1 Connection 2 Connection 3 Connection 4

Component B Component B Component B Component B

Connection 1 Connection 2 Connection 3 Connection 4

Component B Component B Component B Component B

B4 B1

B1 B1

B3 B1

B1 B1

B4 B1

B1 B1

Algorithmic Proliferation

Connection and behavior study on four components Component C Component C Component C Component C

C1 C1

C1 C1 C2

C1 C3

Connection 1 Connection 2 Connection 3 Connection 4

Connection 1 Connection 2 Connection 3 Connection 4 76 Algorithmic Proliferation

Component C Component C Component C Component C

Connection 1 Connection 2 Connection 3 Connection 4

Component C Component C Component C Component C

Connection 1 Connection 2 Connection 3 Connection 4

Component C Component C Component C Component C

C1 C4

C1 C1

Algorithmic Proliferation

Connection and behavior study on four components Component D Component D Component D Component D

D1 D1

Connection 1 Connection 2 Connection 3 Connection 4 D1

Component D Component D Component D Component D

D1 D2

D1 D3

Connection 1 Connection 2 Connection 3 Connection 4

Connection 1 Connection 2 Connection 3 Connection 4 78 Algorithmic Proliferation

Component D Component D Component D Component D

Connection 1 Connection 2 Connection 3 Connection 4

Component D Component D Component D Component D

D1 D1

D2 D2

D1 D1

D3 D3

D4 D4

D1 D1

Algorithmic Proliferation

Connection and behavior study on six components A1 D2

A1 D2

A1 A1

A2 D2

A1 A2 A1

A1 D2

A2 A1

A3 D2

A1 A3

A3 A1

A4 A1 A4

A1 D2

A1 A1

A4 A1

80 Algorithmic Proliferation

A4 A1

C1 A1

C1 C1

A1 D1

C1 A1

A3 C1

A1 D1

C1 A1

A4 C1

D1 C1 Algorithmic Proliferation

Connection and behavior study on nine components D4 B1

B1 D4

B1 A1

A1 B1

B1 A1 D4 B1

B1 A1

A1 A1

A1 B1

D4 B1 A2 D4 B1

D4 B1

A3 D4

A1 A1

A1 B1

D4 B1 A4

Algorithmic Proliferation

A1 B1

B1 A1

A1 A1 A1

A1 D1

D4 A1

D1 A1

A1 D2 A1 A1

D1 D2

D4 D1

D2 A1

A1 A1

D3 A1

D1 D3

D4 D1

A1 A1

D1 D3 D4

D4 D1

D1 D4

A1 A1

D4 Algorithmic Proliferation

Connection and behavior study on nine components A1

B1 A4

A1 A3

B1 A1

A1 A4

A1 A1

A1 A3

A4 A1

A3 D4

D4 A1

A1 A1

A1 A3

B1 A1

A1 D2 A1

D4 B1

A3 A1

A1 A3

B1 A1

84 Algorithmic Proliferation

D4 A3

A1 A1

A1 B1

A3 D4

D4 A1

A1 D4

A1 A1

A1 A4

A3 D4

A4 A1

A1 A4

A1 B4

A3 D4

B4 A1

A1 B4 A1

A1 C4

C4 A1

C4 A1 C4

A1 D4

C4 C4

A1 A1

A1 A3

C4 A1

A1 A1 B4 A1 B4 A1 B4 A3

A1 B4

A1 A1 A4 A1 A4 A1 A4 A3

A3 A4

A1 A1 C4 D4

A1 D4

A1 D4 Algorithmic Proliferation

A1 D1

D1 B1

D4 A1

D1 D4

A1 D4

Template

Polypropylene Model

Top view

Polypropylene Model

Top view

Polypropylene Model

Top view

Connection and behavior study on fifteen components A1 D3

D3 B1

D4 A1

D1 D4

A1 D4

Template

A1 D4

D4 B1

D1 D4

A1 D4

Template

Overlapping Top view

86 Algorithmic Proliferation

Polypropylene Model

Front view

Polypropylene Model

Front view

Polypropylene Model

Front view

Overlapping

Front view Algorithmic Proliferation

D1 A1 D1

A1 D1

D1 A1

A1 D1

Template

Polypropylene Model

Top view

Polypropylene Model

Top view

Polypropylene Model

Top view

Connection and behavior study on sixteen components D1

D1 A1 D2

A1 D2

D2 A1

A1 D1

Template

D1 A1 D4

A1 D4

A1 D1

Template

A1 D1

Overlapping Top view

88 Algorithmic Proliferation

Polypropylene Model

Front view

Polypropylene Model

Front view

Polypropylene Model

Front view

Overlapping

Front view Algorithmic Proliferation

D1 A1 A4

D1 A1

A4 A1

A1 A4

A4 A1

A1 D1

A4 A1

Template

Polypropylene Model

Top view

Polypropylene Model

Top view

Polypropylene Model

Top view

Connection and behavior study on seventeen components D1 A1 B4

D1 A1

B4 A1

A1 B4

B4 A1

A1 D1

B4 A1

Template

D1 A1 D4

D1 A1

D4 A1

A1 D4

D4 A1

Template

D4 A1

Overlapping Top view

90 Algorithmic Proliferation

Polypropylene Model

Front view

Polypropylene Model

Front view

Polypropylene Model

Front view

Overlapping

Front view Algorithmic Proliferation

D4 D3 D4 A1 B4

D4 D4 A1 D1

D4 A1

A4 C1

B4 A1

B4 B4

A3 D4

D1 B3 D4 A1

A1 D4

D4 D4

B3 A1

B4 A4

D4 A1

A4 D4

D4 A4

92 Algorithmic Proliferation

B4 D2

Template

C4 D4

83 Components

D4 B2

A1 D4

D4 A1

D4 D3 D4 A1 B4

D4 A1

A1 D4

D4 A1

C1 B4

A4 D4

B4 B4

B3 D4 A1 D4

B4 D2

D4 A1

D4 D1

D4 A4

B3 A1

B4 A4

D4 A1

A4 D4

D4 A4

Polypropylene Model

Top view Algorithmic Proliferation

Front view

Side view

94 Algorithmic Proliferation

Side view

Back view

Algorithmic Proliferation

Perspective view

Component composition

96 Algorithmic Proliferation

Contact with the ground

Porosity Algorithmic Proliferation

Side view

98 Algorithmic Proliferation

Algorithmic Proliferation

08 _

Re-defining the component

Re-defining the component and adding another piece to the component, in order to give structural support, light control and porosity.

Re-configuration Component

101

Cover Ring

102 Re-configuration Component

Materials: Polypropylene 8mm Screws 2 mm Machine: Laser Machine

Cover

09 _

Algorithmic Proliferation

103

Template

104 Algorithmic Proliferation

40 Components

A1 D1

Top view Algorithmic Proliferation

105

Front view

Side view

106 Algorithmic Proliferation

Side view

Back view

Algorithmic Proliferation

107

Interior space and contact with the ground

Contact with the ground

108 Algorithmic Proliferation

Side view

Perspective view Component Definition

109

10 _

Digital Tectonics

Parametric component scheme. The parametric digital (software grasshopper) model will reproduce local change. Digital modeling of one part of the system with articulation form capacities through manipulation of associative-parametric variables. Motion sequences using catalogs, animation, renderings. Proposal of readjusting the component by digital modeling, variations on the template of the component can be modified minutes before fabrication depending on its readjustment. Grasshopper.

Digital tectonics

111

Physical models have been converted into digital a prototypes, which allows analyzing accurately the behavior of the system. While studying and analyze the system and self-supporting capacity, digital prototypes allow having an exploded of the system. Parameterization allows the transformation of the system into a digital system. This allows introducing changes into the system without actually having to build it. 112 Digital Tectonics

Phase 1

Phase 2

Phase 3

Phase 4

Phase 5 Digital tectonics

113

27 .70 20.54째

R75.38

14.13째

R58.73

8.85 째

54.41

R178.82

32.35

71.95

75.34

째

R258.17

82.86

Measures and geometry extraction working with parametric software (grasshopper) as generator of the digital system, a several steps gave us a component movement curves, 3D component geometry, connections with close neighbor principle, component connection, study mathematical relationship.

114 Digital Tectonics

41.29

44.01

35.32

Grasshopper explanation

Step 1: Template projection.

Step 2: Component movement curves.

The template of the component is projected on an x-y surface and divided into several points.

The movement curves originate from dozens of photos that were taken during the first stage of the design. Eight target points of a single component were studied in order to obtain the right curves. When the component switches from position A to position D, the points in turn move across imaginary lines. These lines in are taken and implement in the Rhino 3D file.

Step 3: Line evaluation.

Step 4: 2D geometry.

The lines are re parametrized and remapped to create a domain from position A to D (respectively 1 to 4). Groups of points are divided and orientated over the eight curves. Now the movement of the slider will transform the groups of points along the curves that were evaluated in the previous step.

A new line is woven through the groups of points. In this way the geometry of the original components appears. Step 1 to step 4 are repeated for each part inner and outer part of the component. Since the component is build up from tree different parts, and each part consist of an inner and outer line. The 4 steps have to be executed six times per component. Digital tectonics

115

Step 5: 3D geometry.

Step 6: Closest neighbor principle.

The lines that were woven through the points have to be lofted and extruded to create the required 3D geometry.

Since a component can only transmit or receive forces from the component it is connected to, one has to research and transmit the movement curves of neighboring components to the 3D environment. For this, one has to study the position and amount of rotation of each surrounding component.

Step 7: Component connections. One component is always surrounded by four components; logically this means there are four other curves. These curves have to be reparametrized and evaluated. A plane is placed on these curves in order to link the geometry of a second, third, fourth and fifth component to this curve.

Step 8: Study of mathematical relationships. Each linked component adds four new movement lines. Since components are under the influence of their four neighbors, the relationship between them has to be studied and mathematically described. This resulting equation determines the amount of influence components have on each other, this is crucial since components with greater distance have less impact.

116 Digital Tectonics

â&#x20AC;&#x153;Self-organization is a set of dynamical mechanisms whereby structures appear at the global level of a system from interactions of its lower-level componentsâ&#x20AC;? -Bonabeau et al, in Swarm Intelligence, 1999

Digital tectonics

117

11 _

Control Engineering

Produce dynamic local change on the component, studding how change one position in to another. Research different systems actuation. There are different types of actuators, such as electrical, mechanical, electromechanical, electronic, hydraulic and pneumatic. Studies how to integrate into the system the actuators. Design of the components and assembly details to fix the actuators.

Control Engineering

119

Heliotropism is a term used for solar tracking: - Ability to turn flower and/or leafs. - Specialized collection of â&#x20AC;&#x153;motorâ&#x20AC;? cells. - Plant cells shrink or grown according to Turgor pressure. - Maximum or minimum amount of sun. - Attract insects for pollination.

120 Control Engineering

DATA

UV LIGHT

SENSORS

NONE (Increasement of potassium ions changes osmic potential)

ACTUATOR

Pulvinus (Motor cells)

RESULTS

Solar tracking

Control Engineering

121

Trigger plants: - Energy from photosynthesis. - Nutrients from insects. - Lure by scent and color. - Distinguish between preys and non preys. - Closes within 0.5 seconds.

122 Control Engineering

DATA

Proximity of insects or small reptiles

SENSORS

Trigger hairs

ACTUATOR

Midrib cells (osmotic collapse)

RESULTS

Entrapment

Control Engineering

123

System behaviour: - Ability to support and adapt to the natural behaviour of urban citizens in park areas. - Ability to sense and react to different stimuli. - Ability to create architectural space. - Ability to facilitate various site related activities. - Ability to function in small or large proliferation.

124 Control Engineering

DATA

UV Light and proximity of people

SENSORS

Light sensors and proximity sensors

ACTUATOR Pistons

RESULTS

Architectural Space

Control Engineering

125

DATA

UV LIGHT

DATA

SENSORS

NONE (Increasement of potassium ions changes osmic potential)

SENSORS

ACTUATOR Pulvinus (Motor cells)

RESULTS

126 Control Engineering

Solar tracking

ACTUATOR

RESULTS

Proximity of insects or small reptiles

DATA

UV Light and proximity of people

Trigger hairs

SENSORS

Light sensors and proximity sensors

Midrib cells (osmotic collapse)

ACTUATOR Pistons

Entrapment

RESULTS

Architectural Space

Control Engineering

127

10 20 30 40 50 60 70 80 W

17 16

Sun path diagram. 20

Difference between solar time and local mean time

Equation of Time [mins ]

10 5 0 -5

80 E

-10

13 -15

-20 Jan S

128 Control Engineering

Feb

Mar

Apr

80 E

May

Jun

Jul Month

Aug

Sep

Oct

Nov S

Dec

17 16

Jan S

Top view Minimum position

Front view

Initial phase the system identifies the person (minimum position)

Sensors Sensors Top view

Front view

Medium position Second phase individual approach of the system (medium position)

Sensors

Sensors Top view

Maximum position Front view

Third phase interaction of the system with person (maximum movement) Analysis of the possible reactive areas in function of the minimum, medium and maximum possible variation. The study is divided in 3 steps: Initial phase the system identifies the person (minimum position), Second phase individual approach of the system (medium position), Third phase interaction of the system with person (maximum movement). Control Engineering

129

12 _

Mechatronics

We are looking for an intelligent architectural design with a robust system that has a series of actuator whose primary function is to generate movement. In the same way that the nervous system controls the muscles of a living vertebrate, the performance of the system is controlled by means of the different stimuli transmitted from the network of sensor. The actuators are devices or subsystems that work by transforming the energy generated, normally by air, water or electricity into some kind of motor action (hydro, pneumatic or electric), in order to generate an effect on an automated process. There are different types of actuators, such as electrical, mechanical, electromechanical, electronic, hydraulic and pneumatic. Study of connecting Grasshopper to Arduino. The phase will focus on the investigation of the most appropriate actuation system and the design of the integration to the mechanic devices to the system.

Mechatronics

131

Position 1

Re-direction the wire 2mm screw

Actuator

132 Mechatronics

Position 3

Position 2

Methacrylate wheel

Steel wire

Packing ring

Arduino Code int myServoPin = 3; int myServoPinA = 5; int myServoPinB = 6; int myServoPinC = 9; int myServoPinD = 10; int myServoPinE = 11; Servo myServo; Servo myServoA; Servo myServoB; Servo myServoC; Servo myServoD; Servo myServoE; int positionServo; int pinSensor=0; int value; void setup(){ myServo.attach(myServoPin); myServoA.attach(myServoPinA); myServoB.attach(myServoPinB); myServoC.attach(myServoPinC); myServoD.attach(myServoPinD); myServoE.attach(myServoPinE); Serial.begin(9600); }

Servo Hitec HS - 422

15 141312 11 10 9 8 7 6 5 4 3 2 1 0

USB

Digital Pins

Arduino diagram FTDI Chip

Voltage Regulator 5V

012345

Analog Input Pins

Power Pins

void loop(){ value = analogRead(1); Serial.println (value); if (value>700){ myServo.write(180); myServoA.write(0); // delay (2000); myServoB.write(0); myServoC.write(0); myServoD.write(0); myServoE.write(0); delay (3000); myServo.write(0); myServoA.write(180); // delay (1000); myServoB.write(180); myServoC.write(180); myServoD.write(180); myServoE.write(180); }

Light sensor

Mechatronics

133

BENCHMARK 13 Polypropylene components 0.8mm. 6 Servos 2 Light Sensors 1 Arduino

134 Mechatronics

Mechatronics

135

BENCHMARK 14 Twintex components 1.0mm. 2 Servos 2 Proximity Sensors 1 Arduino

136 Mechatronics

Mechatronics

137

13 _

Architectonic Application

With the digital model and parametric software (grasshopper) we can control the parameters, limits, laws and possibilities of spaces, we worked with three specific parameters routing, proximity and solar radiation. Analyzing this three parameters we got space distribution, program solution, space organization.

Architectonic Applications

139

Barcelona Green Areas

Key Factors to determine the humanâ&#x20AC;&#x2122;s behavior in natural areas: - ROUTING - PROXIMITY - SOLAR RADIATION

Park Ciudadela

140 Architectonic Applications

People routing about 15 minutes each.

Park Ciudadela

Architectonic Applications

141

People routing about 15 minutes each.

In order to quantify the people, space distribution, program and space organization, we made a table with different categories of people, activities, percentage and radios. Analyzing the park we noticed that there were accounts different types of profile, individuals, couples, and groups, who performed different activities each other. To manage and implement this information, follow different groups of people (individuals, couples and groups) and coded by routing, activity giving a table of values â&#x20AC;&#x2039;â&#x20AC;&#x2039;to quantify people, space and activities in the park

142 Architectonic Applications

PROFILE

DESCRIPTION

PERCENTAGE

COMPONENTS

Loners who look for internal peace and distraction form city life.

35%

1500

25%

2000

Couples

People consisting in lovers, senior citizens or dog walkers.

3 - 10

Usually parents with children and groups of athletes.

20%

3000

Groups at the park for picnics, parties or family gatherings.

15%

4000

Large amount of people who gather together for organized public events or festivals.

6000

1 Individuals

Small groups

10 - 25

SYMBOL

RADIUS

Big groups

25+ Public

Architectonic Applications

143

25% Routing

50% Routing

100% Routing

75% Routing

100% Routing

144 Architectonic Applications

Summer

Fall

Winter Solar radiation Summer

Spring

Architectonic Applications

145

Solar heat map

Closest 1 connections

Function borders

Closest 3 connections

Distance borders

Closest 5 connections

Contact borders

Closest 10 connections

146 Architectonic Applications

Solar heat map + function borders + distance borders + contact borders + closest 3 connections

Architectonic Applications

147

Location borders Contact borders Distance borders Function borders

Contact borders Distance borders Function borders

Distance borders Function borders

Function borders

148 Architectonic Applications

Location borders Contact borders Distance borders Function borders

Contact borders Distance borders Function borders

Distance borders Function borders

Function borders

Architectonic Applications

149

Closest 1 connections

Closest 3 connections

Closest 5 connections

Closest 10 connections

150 Architectonic Applications

Closest 1 connections

Closest 3 connections

Closest 5 connections

Closest 10 connections

Architectonic Applications

151

Location borders Contact borders Distance borders Function borders

Contact borders Distance borders Function borders

Distance borders Function borders

Function borders

152 Architectonic Applications

Function borders

Closest 1 connections

Closest 3 connections

Closest 5 connections

Closest 10 connections

Architectonic Applications

153

Emergence by simple rules: Positioning on green areas Positioning on peak thermal values Positioning in within contact range Positioning within distance boundaries

154 Architectonic Applications

Emergence by simple rules: Positioning on green areas Positioning on peak thermal values Positioning in within contact range Positioning within distance boundaries

Architectonic Applications

155

Multi-Use Specific

Use adaptation and integration.

156 Architectonic Applications

GROUP PROFILE

Individuals

FUNCTIONS

Reading

Meditation

Yoga

Tai Chi

Playground

Fitness

Couples

Small groups

Large groups

Restaurant

Green house

Public Distributor

Festivals

Architectonic Applications

157

GROUP PROFILE

Individuals

Couples

Small groups

Large groups

Public

158 Architectonic Applications

ALGORITHMIC PROLIFERATION

Use adaptation, integration and responsiveness of the system.

Architectonic Applications

159

160 Architectonic Applications

Architectonic Applications

161

162 Architectonic Applications

Architectonic Applications

163

164 Architectonic Applications

Architectonic Applications

165

14 _

Fabrication

When we were scaling up the model we realized that we gave a problem with the material, forces in the 1:1 scale asked for a material that could deal with it. The objective of fabrication is to provide the knowledge to be able to fabricate some pieces of the components with different tools and software; we want to explore the properties of the materials and different manufacture techniques.

Fabrication

167

Big scale

We scale the component 1:1 dealing with wood material, we used CNC machine to cut the piece. In this new phase we insert Pneumatic cylinder (Piston) to analyze the material behavior, deformation, properties and weigh of the component.

168 Fabrication

CNC Machine

Fabrication

169

Materials: 1 Medium-Density Fiberboard (MDF) 0.5mm 8 Screws 8 mm Machine: CNC Machine Perspective

900

Top view 170 Fabrication

Pneumatic cylinder (Piston) Pinstons valves

Air hose Air compressor

Air compressor regulator

Arduino

Computer

Fabrication

171

Materials: 1 10 Actuator: 1 Machine:

172 Fabrication

Medium-Density Fiberboard (MDF) 0.5mm Screws 8 mm Pneumatic cylinder (Piston) CNC Machine

Materials: 1 10 Actuator: 1 Machine:

Medium-Density Fiberboard (MDF) 0.5mm Screws 8 mm Pneumatic cylinder (Piston) CNC Machine

Front view

Minimum position

Front view

Maximum position Fabrication

173

Materials: 1 10 Actuator: 1 Machine:

174 Fabrication

Medium-Density Fiberboard (MDF) 0.5mm Screws 8 mm Pneumatic cylinder (Piston) CNC Machine

Side view

Minimum position

Side view

Maximum position

Perspective view

3 Components Materials: 3 Medium-Density Fiberboard (MDF) 0.5mm 28 Screws 8 mm Machine: CNC Machine

Perspective view

3 Components Fabrication

175

Materials: 3 28 Actuator: 1 Machine:

Medium-Density Fiberboard (MDF) 0.5mm Screws 8 mm Pneumatic cylinder (Piston) CNC Machine

Back view

Minimum position

Middle position

Maximum position

Side view

Minimum position

Middle position

Maximum position

Front view

Minimum position

Middle position

Maximum position

176 Fabrication

Back view

Side view

Front view Fabrication

177

Fabrication workshop

The objective of the workshop is to optimize the manufacture of components to optimize the material and strengthen some critical points of the component and create a composite material. In the 5 days workshop the goal was create a composite material with fiberglass and resin, preserve the continuity of the component and save stock material. When we were scaling up the model we realized that we gave a problem with the material, forces in the 1:1 scale asked for a material that could deal with it and introduction piston to scale 1:1. Tutors: Marco Verde and Jordi Truco.

178 Fabrication

1:1 Scale Benchmark Scale Proliferation Scale

Fabrication

179

Component + piston

First we designed in scale 1:1 in order to preserve the continuity of the component with not lost any quality and properties, introducing pistons to the fabrication in order to preserve the continuity of the component also fabrication in order to save stock material.

180 Fabrication

In order to fabricate the components and save material we divided the ring into two equal parts to take advance of all possible material. We analyzed if they have the same behavior, measure, weight.

Fabrication

181

0 Screws

4 Screws

6 Screws

0 Screws - 52,9g

4 Screws - 58,2g

6 Screws - 60,1g

Determinate the difference in weigh. The screws metal increase weight making relatively strong. By scaling up the component the influence becomes smaller. 182 Fabrication

Testing continuity and changes the component with new perforations / screws and overlap.

Top View

Front View

Side View

Fabrication

183

Minimum position

Middle position

Maximum position

Transforming components a middle position to prevent maximum stresses in the material. Finding the balance between optimal stress reduction and mold fitting.

Minimum position Middle position

Minimum position

184 Fabrication

Minimum position

Middle position

Maximum position

Slopes have to be created to prevent the machine from interfering. Maximum angle canâ&#x20AC;&#x2122;t be crossed.

Fabrication

185

Keeping in mind de constraints of the manufacturing process a solution must be found.

Top View

Side View

Nesting the components to create an optimum path for the milling machine.

Surfaces between the components and edges were created to prevent to machine from interfering with the cast.

186 Fabrication

Creating the stock material from EPS. Cutting in order to create an overlap of the material.

Fabrication

187

Placing the stock in the milling machine.

188 Fabrication

Not the most optional material was used. Further smoothing required. Manual method using sandpaper Glue - stock material failure caused problems

Fabrication

189

The mold covered with glue and aluminum foil as a basis for then setting the resin with glass fiber.

190 Fabrication

The mold covered with glue and aluminum foil

Fabrication

191

Materials acquired for the creation of the composite and the preparing of the mold. Cutters - Brushes - Mask - Syringes - Spates - Plastic - Raisin - Glue - Release agent - Gloves - Cups - Sand paper Composite was created by 4 layers of glass fiber

192 Fabrication

Working process, we put the composite in the mold to dry and adopt the curvature

Fabrication

193

Covered the mold with the composite and waited for some hours to dry

194 Fabrication

Homogenous composite ready to cut. Flaws due to applying raisin and using the stock material.

Fabrication

195

We designed parallel various patterns were created in order to increase porosity of the top component. Randomness fails as a design strategy.

Finding an solution by interacting with the geometry.

New iteration: following specific lines that originate from the existing geometry.

196 Fabrication

Final pattern Holes rotated towards the center Holes find their origin geometry in fabric fasteners. Lines will be engraved in wood not in composite Fabric will be placed continuously

Fabrication

197

Using the same coordinates of the first nesting, we nesting again the borders of the components and the new pattern design

198 Fabrication

Top view composite component

Fabrication

199

200 Fabrication

Fabrication

201

202 Fabrication

Fabrication

203

Materials: 1 Composite (4 layer glass fiber) 8 Screws 12 mm Actuator: 1 Pneumatic cylinder (Piston)

Minimum position

204 Fabrication

Maximum position

Overlapping 3 positions

Fabrication

205

Using the same composite component we insert a fabric to control the porosity and light of the component.

206 Fabrication

Composite Component with fabric

Fabrication

207

Installation in Elisavaâ&#x20AC;&#x2122;s hall

The objective was created a installation in Elisavaâ&#x20AC;&#x2122;s hall. We cut 9 components in CNC machine, 84 screws, we painted all the components, and each component has a codification to be more easy assembly everything.

208 Fabrication

Template 9 components Fabrication

209

We painted all the pieces of the 9 components before the assembly.

210 Fabrication

Drying the pieces

Fabrication

211

Process the assembly in Elisavaâ&#x20AC;&#x2122;s hall.

212 Fabrication

Fabrication

213

214

215

216

217

218

219

220

221

Computational Design Laboratory

01 _

Introduction

Among the significance of digital design is the way that this form of mediated design is beginning to evolve not only unique formal content, but also a unique body of architectural concepts. This structure of design concepts, their link to theories, models, technologies and techniques currently employed in digital design research and digital praxis. Digital architecture uses computer modeling, programming, simulation and imaging to create both virtual forms and physical structures. The terminology has also been used to refer to other aspects of architecture that feature digital technologies. The emergent field is not clearly delineated to this point, and the terminology is also used to apply to digital skins that can be streamed images and have their appearance altered. - Jordi Truco (1), course introduction.

(1) Jordi Truco, Arch. ETSAB, MArch Emtech AA. Partner of HYBRIDa Studio and director of the Advanced Design and Digital Architecture master programme at ELISAVA, Barcelona.

Introduction

225

02 _

Genetic vs. Generative

Since the modern movement began to fade away, which happened at the same time as markedly stylistic historicist, revisions architectural theory has shown great interest in positivist design methodologies. Studies of architectural complexity and dynamic systems have stirred renewed interest in networks, bottom-up methods, adaptive systems, genetics and the automatic creation of form as the fundamentals of a new generation of design techniques.

Genetic vs. Generative

227

Mathematics in nature:

The mathematical consequence of this relationship between nature and mathematics is enormous. We have discovered the limitations of the solution of differential equations by integration in all but the most simplest situations. By using computers to mimic the step by geometric movement of objects, we have discovered chaotic motion. This revolution gives an insight into just how frail our mathematics has been in attempting to understand the secrets of nature. Architecture in future will surely be dependent on various other branches of science and technology. INTER and INTRA dependence of the building architecture, which preciously was considered under civil, presently electrical and mechanical engineering. â&#x20AC;&#x153;Mathematicsâ&#x20AC;? integrated into the building sciences will surely be a great tool to explode various generative forms and iterations as a design part in architecture. It is not that mathematics was not previously integrated to the art field. Concepts of Proportioning like golden ratio, Le Corbusierâ&#x20AC;&#x2122;s human scale etc are used way before the decades. Have you ever wondered how high a flea of the size of a human could jump? Why rivers meander or how high a tree can grow? Mathematics in Nature provides answers to all these questions and many more, while introducing the reader to the ideas and methods of mathematical modeling.

228 Genetic vs. Generative

Symmetry: Many mathematical principles are based on ideals, and apply to an abstract, perfect world. This perfect world of mathematics is reflected in the imperfect physical world, such as in the approximate symmetry of a face divided by an axis along the nose. More symmetrical faces are generally regarded as more aesthetically pleasing.

Symmetry: Five axes of symmetry are traced on the petals of this flower, from each dark purple line on the petal to an imaginary line bisecting the angle between the opposing purple lines. The lines also trace the shape of a star.

Shapes - Perfect: Earth is the perfect shape for minimizing the pull of gravity on its outer edges - a sphere (although centrifugal force from its spin actually makes it an oblate spheroid, flattened at top and bottom). Geometry is the branch of mathâ&#x20AC;&#x2122;s that describes such shapes.

Genetic vs. Generative

229

Shapes - Polyhedral: For a beehive, close packing is important to maximize the use of space. Hexagons fit most closely together without any gaps; so hexagonal wax cells are what bees create to store their eggs and larvae. Hexagons are six-sided polygons, closed, 2 dimensional, many-sided figures with straight edges.

Parallel lines: In mathematics, parallel lines stretch to infinity, neither converging nor diverging. These parallel dunes in the Australian desert arenâ&#x20AC;&#x2122;t perfect - the physical world rarely is.

Pi: Any circle, even the disc of the Sun as viewed from Cappadocia, central Turkey during the 2006 total eclipse, holds that perfect relationship where the circumference divided by the diameter equals pi. First devised (inaccurately) by the Egyptians and Babylonians, the infinite decimal places of pi (approximately 3.1415926...) have been calculated to billions of decimal places.

230 Genetic vs. Generative

Fractals: Many natural objects, such as frost on the branches of a tree, show the relationship where similarity holds at smaller and smaller scales. This fractal nature mimics mathematical fractal shapes where form is repeated at every scale. Fractals, such as the famous Mandelbrot set, cannot be represented by classical geometry.

Fibonacci spiral: If you construct a series of squares with lengths equal to the Fibonacci numbers (1, 2, 3, etc) and trace a line through the diagonals of each square, it forms a Fibonacci spiral. Many examples of the Fibonacci spiral can be seen in nature, including in the chambers of a nautilus shell.

Golden ratio (phi): The ratio of consecutive numbers in the Fibonacci sequence approaches a number known as the golden ratio, or phi (=1.618033989...). The aesthetically appealing ratio is found in much human architecture and plant life. A Golden Spiral formed in a manner similar to the Fibonacci spiral can be found by tracing the seeds of a sunflower from the centre outwards.

Genetic vs. Generative

231

The Voronoi diagram: Informal use of voronoi diagrams can be traced back to Descartes in 1644. Dirichlet used 2 dimensional and 3 dimensional voronoi diagrams in his study of quadratic forms in 1850. British physician John Snow used a voronoi diagram in 1854 to illustrate how the majority of people who died in the Soho cholera epidemic lived closer to the infected Broad Street pump than to any other water pump.

232 Genetic vs. Generative

Voronoi diagrams are named after Russian mathematician Georg Fedoseevich Voronoi (or voronoy) who defined and studied the general n-dimensional case in 1908. Voronoi diagrams that are used in geophysics and meteorology to analyze spatially distributed data (such as rainfall measurements) are called Thiessen polygons after American meteorologist Alfred H. Thiessen.

Itâ&#x20AC;&#x2122;s easy to make a simple voronoi diagram. Just throw a random scattering of points across a plane, connect these sites with lines (linking each point to those which are closest to it), and then bisect each of these lines with a perpendicular. First step is to draw a line connecting adjacent points. Second step is to draw a perpendicular line to the one you just drew in the midpoint of it. Last step is to connect lines, drawn in the second step, in to a network.

The Voronoi in the nature:

Giraffe.

The Tortoise Shell.

The Wings of a Dragonfly. Genetic vs. Generative

233

The Voroni and architecture: This concept can be implemented in generative forms to save the time for Architects and Designers. There are many such applications:

234 Genetic vs. Generative

1. City Planning Like Town- planning in Architecture may be simplified by assigning the site as a nodal point and generating the voronoi diagram as explained above. For example for comparing areas covered by different hospitals, or shops, etc. with Voronoi diagram one can easily determine where is the nearest shop or hospital, and urban planners can study if certain area need a new hospital.

2. Other Uses A. Their structural properties, both in 2D and 3D. B. As a way to subdivide/organize space, based on proximity/closest neighbor. C. The fact that they can describe many natural formations, like soap bubbles, sponges or bone cells, which can inform architecture with new ways to organize and structure space.

Hangzhou architect slightly

Architect Lab Architecture

Architect Thom Faulders Genetic vs. Generative

235

03 _

Data collection and site study

The intervention site will be the Plaza Lesseps. Collect specific documentation for analysis, plans, and photographs. We understand that the space recently renovated is the site for intervention.

Data collection and Site study

237

Plaza Lesspes

238 Data collection and Site study

Data Collection:

The first phase of the project is to analyze and extract useful data in order to have foundation and unique data of the square. Plaza Lesseps is a square serving as the border between the Sarrià - Sant Gervasi and Gràcia district of Barcelona, loosely divided in two parts. It’s one of busiest places of the city. The square is an attractor point of private and public transport and pedestrians, also have a long trajectory of renovation, design and intervention, but still remains much problems to be resolved, With the team we realize the necessity to analyze the water behaves on, and how leaves from the system. The main subject of research is: the water and its relation to the plaza. The question that is formulated: “In which way, and how fast does the water leaves the system?” When one is researching the subject of water, one has to keep in mind that the path the water follows is equally important as its points of accumulation. Both are unmissable for the following steps, where the collected data will be transformed and used as input for further actions. The team collected several data related to the behaviour of water on the site, maps, diagrams and photographs. We created several maps and diagrams in relation to the research subject. The data collected and presented in these diagrams were uses in Operative Strategies.

Plaza Lesseps

Data collection and Site study

239

240 Data collection and Site study

Data collection and Site study

241

Porosity

242 Data collection and Site study

Texture

Natural drainage point

Slope

Drainage point

Artificial drainage point

Data collection and Site study

243

Porosity The amount of porosity is determined by the density of the material.

244 Data collection and Site study

Texture The rough a material is the more impact it has on the water.

Natural drainage point These are the natural locations where the water leaves the plaza.

Slope

Drainage point

Artificial drainage point

The higher the angle of inclination, the faster the water will move.

These are the natural locations where the water leaves the plaza.

These are the artificial locations where the water leaves the plaza.

Data collection and Site study

245





 





 

 









 











 

 





 





 











 





 



 











 







 



   

  











 







  



Site









Heightlines 



Natural Drainage points(trees)

 





  



Natural Drainage points(grass)

Artificial Drainage (street Drainage)

Artificial Drainage (drainage points)

Wood Areas

Sand Areas

Grids Areas

246 Data collection and Site study

Artificial and Natural drainage points Data collection and Site study

247

04 _

Operative strategies

After studding the site, is required the generation of diagrams for a further dynamic scenario, the task will consist on making on analysis through experimentation. Instead of collecting data and extracting conclusions, the analysis will be done by setting up a dynamic test.

Operative Strategies

249

250 Operative Strategies

The operative scenario consist of transforming the collected data into a cartographic map, with this new data the computer can use to process and run the animate scenario. The team got 5 steps to transform the reality in a matrix of codes, so the computer with processing software can use to run the animate scenario. Step 1: Creation of the grid Step 2: Codification of the data Step 3: Expression of the code into values. Step 4: Expression of the code into directions. Step 5: Determination of the location of the Attractor Point.

The team codified the system in a way that when rain falls, it can move into different directions. The direction of the inclination determines the way the rain moves, and the amount of slope, texture, and porosity influence the speed of the fallen water.

CODIFICATION TABLE: EIGHT DIRECTIONS:

Provides labels for each corresponding surface quality.

E W

Provides the value for each corresponding table.

The directions, in which the data is able to move.

Provides the value for each corresponding combination of labels.

Operative Strategies

251

STEP 1: Creating a grid

252 Operative Strategies

STEP 1: Creating a grid

The grid consisted of cells with a real life size of 10 by 10 meters. To cover the whole area the team provided more than 950 cells and each cell will have new data information combining slop, texture and porosity.

Operative Strategies

253

STEP 2: Codification of the data

B1a Se

B1a Se B1a Se B2ß Se B1a B1a Se Se B1a B1a Se Se A2ß B1a Ne Se

B1a Se B1a B1a B1a E E Se

A2ß Ne A2ß A2ß Ne N

B1a Sw

A2ß Ne

B1a Sw

B1a Sw B1a A1a Sw Se B1a A1a A1a Sw Se Se B1a A1a A1a Sw Se Se B1a A1a A1a S Se Se B1a A1a S Se A1a B1a Sw Se A1a A1a B1a Sw W Se B1a A1a S Se B1a Se B1a A2ß Se Sw B1a A2ß A2ß Se S Sw B1a A2ß Se Sw A2ß A2ß Se Se A1a A1a A1a A1a A1a A1a A1a A1a E E E E E E Ne Ne A1a A1a A1a A1a A1a A1a A1a A1a Ne Ne Ne Ne Ne Ne Ne Ne A1a A2ß A2ß A1a A2ß A2ß A2ß A2ß Ne Ne Ne Ne Ne Ne Ne Ne A2ß A2ß A2ß A2ß A2ß Ne Ne N Ne Ne A2ß A2ß A2ß A2ß A2ß Ne Ne N Ne Ne A2ß A2ß A2ß A2ß A2ß A2ß Ne Ne E E E E A2ß A2ß A2ß A2ß Ne Ne Ne Ne A2ß A2ß Ne Ne

254 Operative Strategies

A2ß Ne

B1a Sw

A1a Se

A2ß Sw A2ß Sw A2ß Sw A2ß Sw B1a W C1a Ne A2ß Ne A2ß Ne A2ß Ne

B1a Sw B1a B1a S Se B2ß B2ß Sw Se

A1a Se A1a Se A2ß Sw A2ß Sw A2ß Sw A2ß Sw A2ß Sw C1a Ne C1a Ne A2ß Ne A2ß Ne

A1a Se A1a Se A1a E A2ß Sw A2ß Sw A2ß Sw C1a Ne C1a Ne A2ß Ne B1a Se

B2ß Se B1a Se B1a Se B2ß Se B2ß Se B2ß Se B1a Se B2ß Se

B1a Se B2ß Se B2ß Se B1a Se B2ß Se B2ß Se B2ß Se B1a Se B2ß Se

B2ß Se B2ß Se B1a Se B2ß Se B2ß Se B2ß Se B1a Se B2ß Se

B2ß Se B1a Se B1a Se B2ß Se B2ß Se B1a Se B1a S B2ß Sw B2ß Sw

B2ß Se B1a Se B1a Se B2ß Se B2ß Se B1a S B2ß Sw

B2ß Se B2ß Se B1a Se B2ß Se B2ß Se A2ß S A2ß Se A2ß S A2ß S A2ß Sw A2ß A2ß S Sw A2ß S A2ß S A2ß A2ß Se S A2ß S A2ß Se

B2ß Se B2ß Se B2ß B2ß B1a B2ß Sw Sw Se S B2ß B1a B2ß B2ß B1a Sw Sw Sw S Se B2ß B1a B2ß E W W Ne B2ß B1a B2ß B2ß B2ß N Nw Nw Nw A2ß A2ß B2ß B1a B2ß N Nw Nw S N A2ß C4d B2ß B2ß B1a N Nw Nw Sw N A2ß A2ß C4d B2ß B1a N S S Sw Se A2ß C4d B2ß S Sw Sw A2ß A2ß C4d S Sw Sw A2ß A2ß W W A2ß A2ß Nw Sw B2ß B4d Sw Sw B2ß B4d B4d A2ß Sw Sw Sw S B2ß B4d B4d B2ß W Sw Sw Sw B2ß B2ß B4d B2ß B2ß S S Sw Sw Sw B1a B2ß B2ß B4d B2ß B2ß Se S S Sw Sw Sw B1a B2ß B2ß B4d B2ß B2ß Se S S S S Sw B2ß B1a B4d B4d B4d B1a Se Se S S S Sw D2ß B2ß B1a B4d B1a C2ß Se Se Se S Sw S B2ß B2ß B2ß B1a C2ß Se S Se Se Se B2ß B2ß A1a A1a B1a C2ß Se Se Se Se Se S B2ß A1a A1a B2ß B1a B1a Se Se Se Se Se Se A4d A1a B2ß B2ß B2ß B1a Se Se Se Se Se Se B2ß B4d B4d B2ß B2ß B2ß E Ne Se E Se Se B2ß B4d B4d B2ß Ne Ne Ne Se A2ß Ne

B1a Sw B1a B2ß Sw Sw B2ß B1a B2ß B2ß Sw Sw Sw Sw B2ß B1a B2ß B2ß Sw Sw Sw Sw B1a B2ß Sw Sw B2ß S B1a Se B2ß B1a Se Se B2ß B1a Se Se B2ß B1a Se Se B2ß B1a Se Se B2ß B1a Se Se B2ß B1a Se Se B1a Se B2ß Se D2ß Se D2ß D2ß Se Se D2ß Se B2ß B2ß Se Se A1a B2ß B2ß B2ß S E E Se A1a B2ß A2ß A1a A4d Se E E E Se B2ß B1a B2ß A2ß A2ß A2ß A2ß Ne E Ne Ne Ne Sw S A2ß A2ß D3? D3? D3? A2ß A2ß Sw W Ne Ne Ne Ne Ne D3? D3? A2ß A2ß A2ß Ne Ne Ne Ne Ne C1a C1a A2ß A2ß A2ß A2ß C1a Ne Ne Ne Ne Ne Ne Se C1a A2ß A2ß A2ß A2ß Ne Ne Ne Ne S C1a A2ß A2ß Se Ne Ne B1a S B1a Se

B1a S B1a Se

B1a S B1a S

B1a S B1a B1a S Se B1a Se

C1a S C1a Se

C1a S C1a S C1a C1a C1a S S S C1a C1a C1a S S S C1a C1a C1a S S S

B1a Se

B2ß Sw B1a Sw B2ß Sw B2ß Nw B2ß Nw B2ß Nw B1a Nw B1a N A2ß W A2ß Sw B2ß Sw B2ß Sw B2ß Sw B2ß Sw B1a S B1a Sw C2ß S C2ß S

C4d S C2ß S C2ß Se B1a Se B1a Se B2ß Se B2ß Se

D1a Se D1a D1a Se Se D1a Se B1a S B1a Se B2ß Sw B2ß Sw B1a Sw

B1a Se B2ß B1a Sw Se B1a B1a Sw Se

B2ß B2ß Ne Nw B2ß B2ß B2ß Nw Ne B2ß B2ß B2ß Nw Sw Ne B1a B2ß B1a Nw S Sw A1a A1a D3? S S Sw A1a A1a D3? S S Sw A1a A1a C2ß S S Sw B1a B1a C2ß S Sw Sw C2ß B1a B1a S Sw Se B1a C2ß C2ß Sw Se Se C2ß C2ß C4d Se Se Se C2ß C2ß C2ß Se Se Se C2ß C2ß C2ß Se Se Se C2ß C2ß C2ß Se Se S C4d C2ß C2ß S S Sw C4d C2ß S Sw B2ß Se B1a B2ß Se Se B1a B1a B2ß Se Se Se B2ß B1a Se Se B2ß B1a B2ß Se S S B2ß B1a B2ß S S S B2ß B1a B2ß S S S B2ß B1a B2ß S S Sw B2ß B1a S S B2ß B1a S S B2ß B1a S S B2ß B1a S S B2ß B1a B1a S Se S B1a B1a S S B1a B1a S S B2ß B1a Se S B1a S B1a S

B2ß Ne B2ß Ne B2ß Ne

B1a Ne

D3? Sw

D3? Sw C2ß Se C4d Se C4d Se

C4d Se C2ß Se C2ß Se C2ß S C2ß Sw

B2ß W B1a Nw B2ß W B2ß W

B1a S B1a S

B1a S B1a Se

B1a Se A2ß Se B2ß E B2ß Ne B2ß Ne

B1a Se B2ß B2ß E E B2ß B2ß Ne Ne B2ß C1a Ne Sw B2ß C1a C1a Ne Sw Sw C1a Sw D3? D3? C2ß Sw Sw Se D3? C2ß C2ß Sw Se Se C2ß C2ß C4d Se Se Se C2ß C2ß C4d Se Se Se C4d C4d C4d Se Se Se C4d C4d C4d Se Se Se C4d Se C4d Se C4d S C2ß Sw

C4d Se C4d Se C2ß Sw

B2ß Nw B1a B2ß Nw Nw B1a B2ß Nw N B1a Nw

C4d Se C2ß Sw C2ß W

A2ß A2ß S Sw B1a Sw C1a B1a Sw Sw C1a C1a Sw Se C2ß C2ß Sw Se C2ß A2ß Se Se C2ß A2ß Se Se C4d C2ß Se Se C4d C4d Se Se C4d C4d Se Se C4d C4d Se Se C4d C4d Se S C4d C2ß S Sw C2ß S A1a Se

A2ß Sw B1a Sw B1a Sw A2ß Sw A2ß Se C1a Se A2ß Se A2ß Se A2ß Se C4d Se C4d Se C4d Se C2ß Sw

A2ß Sw B1a Sw A2ß Sw A2ß Sw A2ß Se A2ß Se C1a Se A2ß Se A2ß Se C4d Se C4d Se C2ß Sw

A2ß Sw B1a Sw B1a Sw B1a Se A2ß Sw

B1a C1a Nw Sw B1a Nw B1a Nw B1a B1a N Nw B1a Nw B1a B1a N Nw B1a Nw

C1a Sw

B1a Nw B1a N

B1a Se

A2ß Se A2ß Se C1a A2ß Se Se A2ß C1a Se Se C4d C2ß Se Se C2ß C2ß Sw Se C2ß Se

A1a S A1a A1a S E A1a S A1a A1a S E A1a Se

C1a C1a Sw Sw

A2ß Sw B1a Sw B1a Sw A2ß Sw

A2ß Sw B1a Sw B1a Sw

A2ß Sw B1a Sw B1a Sw A2ß Sw

A2ß Sw B1a Sw B1a Sw

A1a B1a Sw Se A1a A1a B1a A2ß A2ß Sw W S S Se B1a A1a A1a Sw Sw Sw A1a A1a Sw Sw C1a Se C1a Se C2ß C1a Se Se C2ß C2ß C1a Se Se Se C2ß C2ß Se Se C2ß C2ß Se Se C2ß Se

A1a S A1a A1a S E A1a Sw C1a C1a Sw Sw C1a C1a Sw Sw

B1a Nw

A2ß Sw B1a Sw B1a Sw A2ß Sw

D1a Se D1a D1a Se Se D1a Se A2ß B1a Sw Sw B1a B1a Sw Sw B1a B1a Sw Sw A2ß Sw

C1a C1a Sw Sw A1a S A1a Sw A1a S A1a Sw

C1a Se C2ß Se C2ß Se C2ß Se

C1a Se C1a Se C2ß Se C2ß Se

C2ß S C2ß S C1a Sw C1a C1a Sw W

A1a Sw A1a A1a Sw Sw

C1a Se C1a Se C2ß Se C2ß Se C2ß S C2ß S C1a Sw

C1a S C1a Se C1a E C2ß Se C1a Sw C1a Sw

A2ß S B1a Sw B1a Sw A2ß Sw

A2ß Sw B1a Sw B1a Sw D1a Se

A2ß Sw B1a Sw B1a Sw A2ß Sw D1a Se

A2ß Sw B1a Sw B1a Sw

D1a S D1a Se

A1a A1a S Sw A1a A1a A1a Sw W W

C1a Se C1a Sw C1a Sw

A2ß Sw B1a Sw B1a Sw A2ß W

D1a Se

C1a Sw C1a C1a Sw Sw

C1a C1a Sw Sw C1a Sw C1a C1a Se Se C1ß C1a Se Se C1a Se

C1a Se

Sw Sw Sw B1a B1a Sw Sw B2ß Sw

B2ß B2ß B2ß Sw Sw S B1a B2ß B2ß B1a S Sw Sw Sw B2ß B1a W W B2ß B1a B2ß B2ß N Nw Nw Nw A2ß B2ß B1a B2ß B2ß N N Nw Nw Nw C4d B2ß B2ß B1a B2ß N N Nw Nw Nw A2ß C4d B2ß B1a B2ß N Nw S Se S A2ß C4d B2ß B1a S Sw Sw Nw A2ß A2ß C4d B1a S Sw Sw N A2ß A2ß A2ß W W W A2ß A2ß A2ß Nw Sw Sw B2ß B4d B2ß Sw Sw Sw B2ß B4d B4d B2ß Sw Sw Sw Sw B2ß B4d B4d B2ß B2ß W Sw Sw Sw Sw B2ß B4d B2ß B2ß B2ß S Sw Sw Sw Sw B2ß B4d B2ß B2ß B1a S Sw Sw Sw S B2ß B4d B2ß B2ß B1a S S S Sw Sw B4d B4d B4d B1a C2ß S S S Sw S B1a B4d B1a C2ß C2ß S Se S Sw S B2ß B1a C2ß C4d Se S S Se A1a A1a B1a C2ß C2ß S Se Se Se S A1a B2ß B1a B1a C2ß Se Se Se Se Se B2ß B2ß B2ß B1a B1a Se Se Se Se Se B4d B2ß B2ß B2ß B1a

B2ß Ne B2ß Ne B2ß Ne

B1a S B1a Se

B1a Se A2ß Se B2ß E B2ß Ne B2ß Ne

B2ß E B2ß Ne B2ß Ne B2ß C1a Ne Sw

STEP 2: Codification of theA2ß data Sw

B1a A2ß A2ß Se S Sw B2ß B1a E Sw B2ß C1a B1a Ne Sw Sw C1a C1a C1a Sw Sw Se C1a C2ß C2ß Sw Sw Se C1a C2ß A2ß Sw Se Se C2ß C2ß A2ß Se Se Se C2ß C4d C2ß Se Se Se C4d C4d C4d Se Se Se C4d C4d C4d Se Se Se C4d C4d C4d Se Se Se C4d C4d C4d Se Se S C4d C4d C2ß Se S Sw C2ß C2ß Sw S A1a C2ß Se W

A2ß Sw B1a Sw B1a Sw A2ß Sw

A2ß Sw B1a Sw A2ß Sw A2ß Sw A2ß Se A2ß Se C1a Se A2ß Se A2ß Se

A2ß Sw B1a Sw B1a Sw A2ß Sw

B1a Sw B1a Sw B1a Se A2ß B1a Sw Se

A2ß Sw B1a Sw B1a Sw

B2ß B1a B2ß A2ß Ne Nw Se Se A2ß B1a C1a B2ß B2ß B2ß Se Se Nw Ne Se B2ß B2ß B2ß B1a A2ß A2ß Se Se Nw Sw Ne Ne A2ß C1a A2ß A1a B1a B2ß B1a D3? D3? Se Nw S Sw Se Se Sw Sw Sw A2ß A2ß C1a A1a A1a D3? D3? D3? C2ß Se Se Se S S Sw Sw Sw Se A1a A1a D3? D3? C2ß C2ß C4d C4d C4d C2ß C1a Se Se Se Se Se S S Sw Sw Se Se A1a A1a C2ß C2ß C2ß C2ß C4d C4d C2ß C2ß C1a Se Se Sw Se Se S S Sw Se Se Se B1a B1a C2ß C4d C4d C4d C2ß C2ß C4d C2ß Se Sw S Sw Sw Se Se Se Se Se C2ß B1a B1a C2ß C4d C4d C4d C2ß Sw Se S Sw Se Se Se Se B1a C2ß C2ß C4d C4d C4d Sw Se Se Se Se Se C2ß C2ß C4d C2ß C4d C4d Se Se Se Se Se Se C2ß C2ß C2ß C2ß C4d C2ß Se Se Se Se S Sw The transformation of the information belongs to the side, into a label that contained all C2ß C2ß C2ßEach C2ß A1a of slope, texture and the data. labelC2ß contains all the material properties, amount Se Se Se S Sw S porosity. And the direction of the inclination. C2ß C2ß C2ß C2ß A1a A1a Se Se S Sw S E A1a C4d C2ß C2ß S S S Sw A1a A1a C4d C2ß S Sw S E Operative Strategies 255 A1a B2ß Se Se A1a B1a B2ß

S B S B S A S

B S A S

C S C S C S C S

STEP 3: Expression of the code in a value

162 168

162 162

162 168

162

262

162

168 162 162 168

162 162 162

262 262

162 162

162 162 168 162 168 168

262

162

162 162 168 168 162 168 162 168 168 168 162 168

118

262 118 118 162 162 162

162

168 162 168 168 162 162 168

118

118 162 162 162 162 162 118

162

118

168 162 168 168 162 168 168

118 118

168 162 168 168 162 168

168 168 162 168 168 162

168 162 168 168 162 168

118 118 162 162 162 162 118

168 168 162 162

168 162 168 168

118 118 230 162 168 168 168 162

112 112 112 112

162

112 112 112

218 218 218 212

268 168 162 180 180 180 162 218 218 218 218 218 230 218 218

112

218 218 212 212 112

162 218 230 218 218 218 218

218 212 212 212 212

162 118 118 118 118 274 274 118 118

162 212 212 212 212 118 118 118 118 212

112 212 212

168

212

168 162 168 168 162 168

212

168 162 168 168 162 168

118 118 118

162

118 118 118

118 118

162

118 118 118 118 118 118

162

118 118 118 118

162

118 118

162

212

168 162

162 212

112

212

168 162

162

112 112

212

168 162

162

212

168 162

162 162

212

168 162 162

212 212

162

162 162

212

162 162

162

212 212 212

168 162

212 212 212 212 212 212

162 162 162 162

112

162

112

168 162 168

162 162

162

112

212

162

256 Operative Strategies

212 212 212

162 162 162

212 212 212 212

212 212 112

168 162 168 162 168

112 112 112 112 112 112 112 112 212 212 212 118 118 118 118

118 118 118 118 118 118 162

212 212 212 212 212

112 112

112 112 112 112 112 112 112 112 118 118 118 118 118 112 118 118

218 218 212 212 212 212 112

168 168 162

118 118 118 118

218 218 218 212 212

112

168 168 162 162 168

162 118 118 118 118 118 118 118 274 274 274 118 118 180 180 168

212 212

112 112

168 118 112 130 130 112 168 168 168 162 162 168

118 118 112 112

212

218 218 212

112

118 118 118 118 168 162 168 118 118 118 118 168 180 180 168 168 168 162 162 168

162

218 218 212 212

112 112

168 168 168 168 112 112 168 162 162 218 230 218

118 112 112

162

218

168 168 168 168 112 112 162 218 218 230 218 218

112 112

162 112

218 218 212

168 162 168 168 180 168 168 162 218 218 230 218 230 230 218

268 168 168 168

112

218 218 212

162 162 168 168 180 168 168 162 162 218 218 230 230 230 230 230 218

268 268 268 168 162 180 162 218 218 218 218 218 218 218

112 112

218 218 212

168 180 180 168 168 162 162 218 230 230 230 230 230 230 230 218 168 168 180 168 168 168 162 162 218 230 230 230 230 230 230 218

168 162

112 112

162

168 180 168 118 118 274 274 218 218 230 230 230 230 230 230 218 212 168 180 180 168 118 118 218 218 218 218 230 230 230 230 230 218 218 212

118 118

168 162

162

274 274 218 218 118 118 118 212 118 112 112

118 118 118 162 168 162

118 118 118 118 118 274 274 274 218 218 230 218 118 118 118 212

118

162 112

112 112

162 112 112

118 118

168 162

112

162 118 118

212 218 118 118 212 118

118

168 162

112 112 112 162

118 230 168 162 168 168 168 168 168 212 212 218 218 212 118 118

168 162

112 112

168 168 168 168 212 162 118 118 118 162

118

168 162

262

162

118 118

162

162 162

168 168 168 168 212 212 212 118 118

118 118 118 230 168 162 168 168

162

168 168 168

162 162 168

162

262

118 162 118 118 162 162 162 118

168 168 162

162

262

118 118 162 162 162 118

162

118 118 230 168 168 162 168

162 162 168

162

168 162

262

118 162 162 162 162 118

162

118 118 118 168 162 168 168

168 162 168 168

118 118 162 162 162 162 118

162

118 168 168 162 168 168

162 168 168

162

118 118 162 162 162 162 118 262

162

162 162 168 168 162 168 168 162 168 162 168 168 118 168

118 118 118 162

262 262

162

162 168 168 168 162 168

118

262

162

8 168

118 118 118 168 162 168 168

118 118 230 168 168 162 168

168 168 168

162 1

168 168 168 168 212 162 1 STEP 3: Expression of the code in a value

118 118 118 230 168 162 168 168

168 168 168 168 212 212 212 1

118 118

118 230 168 162 168 168 168 168 168 212 212 218 218 2

118

118 118 230 162 168 168 168 162

118

118 118 118 162 168 162

118 118

118 118 118 118 118 274 274 274 218 218 230 218 1

118

168 180 168 118 118 274 274 218 218 230 230 230 2

8 162

118 118

212 218 118 1

274 274 218 218 118 1

168 180 180 168 118 118 218 218 218 218 230 230 230 2

168 180 180 168 168 162 162 218 230 230 230 230 230 230 2

168 162

168 168 180 168 168 168 162 162 218 230 230 230 230 230 230 2

168 162

162 162 168 168 180 168 168 162 162 218 218 230 230 230 230 230 218 168 162 168 168 180 168 168 162 218 218 230 218 230 230 218

218

268 168 162 180 180 180 162 218 218 218 218 218 230 218 218

112

268 268 268 168 162 180 162 218 218 218 218 218 218 218 268 168 168 168

162 218 230 218 218 218 218

168 168 168 168 112 112 162 218 218 230 218 218 168 168 168 168 112 112 168 162 162 218 230 218

8 118 112 130 130 112 168 168 168 162 162 168

Each label was turned into a numerical value. The values are based on the magnitude of corresponding code.

8 118 118 118 168 180 180 168 168 168 162 162 168

4 274 118 118 180 180 168 118 118 118 118

8 118 118 212

168 168 162 162 168 168 168 162

168

168 162 168 162 168 Operative Strategies

212

168 162 168 168 162 168

212

168 162 168 168 162 168

257

STEP 4: Expression of the code into directions

258 Operative Strategies

STEP 4: Expression of the code into directions

Each label was contained besides the values, also the direction of the inclination of the cell. In this way, following the chain of arrows, the precise location of the water accumulation could be traced.

Operative Strategies

259

STEP 5: Determination the location of the attractor point

CARTOGRAPHY MAP

260 Operative Strategies

STEP 5: Determination the location of the attractor point

The attractor points where placed where the chain of directional arrows ended or where several clashed together. Since this meant that the water was accumulating on this specific spot. The amount of vectors (direction and value) that ended up in the attractor point, were added together, so that attraction force of the points could be calculated.

Operative Strategies

261

262 Operative Strategies

Cartography map Operative Strategies

263

05 _

Animated scenario

In this phase we will produce 3D motion polystructures. Tridimensional diagrams, sedimentation of particles, interconnected network. This is the moment to star analyzing the phenomenon, and tiring to find the way to bring the system to an equilibrium situation.

Animated Scenario

265

118 168 168 162 168 168

162 168 168

2 168 168

118 118 118 168 162 168 168

8 168

118 118 230 168 168 162 168

118 162 118 118 162 162 162 118 168 168 168

162

112 112

168 168 168 168 212 162 118 118 118 162 168 168 168 168 212 212 212 118 118

118 118 118 230 168 162 168 168

162 162

118 230 168 162 168 168 168 168 168 212 212 218 218 212 118 118

118

118 118 230 162 168 168 168 162

118

118 118 118 162 168 162

118 118

118 118 118 118 118 274 274 274 218 218 230 218 118 118 118 212

162 118 118

212 218 118 118 212 118

112 112

162 112 112

274 274 218 218 118 118 118 212 118 112 112

168 180 180 168 118 118 218 218 218 218 230 230 230 230 230 218 218 212

118 118

218 218 212

168 180 180 168 168 162 162 218 230 230 230 230 230 230 230 218

168 162

218 218 212

168 168 180 168 168 168 162 162 218 230 230 230 230 230 230 218

168 162

112

168 180 168 118 118 274 274 218 218 230 230 230 230 230 230 218 212

118

168 162

112 112 112 162

118 118

8 162

262

162 162 168 168 180 168 168 162 162 218 218 230 230 230 230 230 218

218 218 212

168 162 168 168 180 168 168 162 218 218 230 218 230 230 218

218 218 218 212

218

218 218 212 212 168 162of180 162 218 218 112 218 particle 218 218 230 the 180 180scenario 268 phase In the animate the 218 team218decided system, because these systems are often used to simulate 212 212 212 218 218 168 162 112 218 218 218 218 218 218 180 218 268 268 268 162 phenomena that occur in the natural world. 268 168 168 168

162 218 230 218 218 218 218

218 218 212

112 112

212 212

212 212 212 212 112 in every grid cell a particle 218 The start setup of the scenario included the previously created grid, and emitter, which, since 212 212 218 218 218 112 112 statement 1 applies here, emits an equal amount of particles for every cell in other words, because waterfalls equally 112 168 168 168 amount of particles at the starting point.. 218 218 212 212 212 212 118 112 130 130 112each everywhere, cell168 has162 the162 same 168 168 168 168 112 112 162 218 218 230 218 218

168 168 168 168 112 112 168 162 162 218 230 218 168

8 118 118 118 118 168 180 180 168 168 168 162 162 168

8 118

212 212 212

112

212 212

212 When the scenario begins to run, each particle behaves according to a fixed set112of 112 rules, and so, since statement 2 applies 212 168 168 168 162 In other here, attributes. words, since water behaves differently each particle has a 212 112 212everywhere, 118 118 118each 118 particle has its own different behaviour. 212 212 212 112 168 162 168 162 168 118 118 212

8 118

212 212

168 168 162 162 168

4 274 274 118 118 180 180 168

212 212

168 162 168 168 162 168

112

The start for fixed, operated from the 212 1ste frame until the112last is different for every single 212of each particle is there 168 168way 162 168the 162 it168 one. They 212 are attracted by the attractor and are 212the 212site itself. 168 162points, 168 162 limited by 112 212

168 162

162 212

112

212

168 162

162

112 112

212

168 162

162

212

168 162

162 162

212

168 162 162

212

162 162

Data labels 162

162

212

162 162

162

212 212 212

168 162

212 212 212

162 162

212 212 212

266 Animated Scenario

162 particle Starting point

162 162 162 162

Grid cells Code

162 162 162

Direction

Value

Location Attractor point

Speed particles

Animated Scenario

267

FRAME 0

TOP VIEW

ISO VIEW

268 Animated Scenario

FRAME 5

FRAME 10

FRAME 15

FRAME 20

FRAME 25

TOP VIEW

ISO VIEW

Animated Scenario

269

Attractor A1; float MASS_ATTRACTOR_1= 12.5; float GRAVITY_1 = 10; PVector POSITION_A_1 = new PVector(220, 210, 0); Attractor A2; float MASS_ATTRACTOR_2= 7.5; float GRAVITY_2 = 10; PVector POSITION_A_2 = new PVector(360, 360, 0); Attractor A3; float MASS_ATTRACTOR_3= 5; float GRAVITY_3 = 10 ; PVector POSITION_A_3 = new PVector(280, 140, 0); Attractor A4; float MASS_ATTRACTOR_4= 3.75; float GRAVITY_4 = 10; PVector POSITION_A_4 = new PVector(215, 380, 0); Attractor A5; float MASS_ATTRACTOR_5= 6.75; float GRAVITY_5 = 10; PVector POSITION_A_5 = new PVector(320, 245, 0); Attractor A6; float MASS_ATTRACTOR_6= 1.25; float GRAVITY_6 = 10; PVector POSITION_A_6 = new PVector(210, 300, 0); Attractor A7; float MASS_ATTRACTOR_7= 1.25; float GRAVITY_7 = 10; PVector POSITION_A_7 = new PVector(190, 280, 0); Attractor A8; float MASS_ATTRACTOR_8= 10; float GRAVITY_8 = 10; PVector POSITION_A_8 = new PVector(120, 160, 0);

270 Animated Scenario

Animated Scenario

271

Particles without tracers

TOP VIEW

Final scenario / Last frame

ISO VIEW

Particles with tracers

TOP VIEW

272 Animated Scenario

ISO VIEW

Final scenario / Last frame Animated Scenario

273

Particles without tracers

274 Animated Scenario

Final scenario / Last frame

Final scenario / Last Frame Render Animated Scenario

275

06 _

Intelligent patterns

Several exercises (using few points) on how to connect points, lines and polylines on certain order may have to be done. Afterwards one of those examples will be applied to the system on the whole set of frames. First motion morphologies will appear. By applying the algorithm we will simplify the diagrams and make them more comprehensive in terms of structure. This new poly structures will start defining better the resultant morphologies in the site.

Intelligent Patterns

277

Final scenario / Polystructures

278 Intelligent Patterns

The selection of lines was done to reduce information, reduction of lines was based on route length and number of particles, the software used was grasshopper

Intelligent Patterns

279

ISO VIEW

TOP VIEW

ISO VIEW

TOP VIEW

ISO VIEW

TOP VIEW

280 Intelligent Patterns

The line is the path the particles travel. Each frame that is captured shows a different position of the particle, en thus a different point in space and time. The team worked in the idea of generated the different patterns using the information of the line, the idea was connecting dots.

p6 p10

p4 p9 p3 p8 p2 p7 p1 p6

Particle 2 Vel. Y Position per frame p5

Particle 1 Vel. X Position per frame

Intelligent Patterns

281

Catalog of possible connections

TOP VIEW

282 Intelligent Patterns

The idea of continuing to work was to select three lines to generate the rules for connection and to generate a catalog of the possibility of continuing with the project.

Frame Velocity Position Distance

Particle 1 3 4 5 6 7 5 5 5 5 5 p1( x,y,z) p1( x,y,z) p1( x,y,z) p1( x,y,z) p1( x,y,z) p1( x,y,z) p1( x,y,z) 0 5 10 15 20 25 30

Frame Velocity Position Distance

Particle 2 3 4 5 6 7 3 3 3 3 3 p2( x,y,z) p2( x,y,z) p2( x,y,z) p2( x,y,z) p2( x,y,z) p2( x,y,z) p2( x,y,z) 3 6 9 12 15 18 21

1 5

1 3

2 5

2 3

Intelligent Patterns

283

Pattern 01 // Top view

Side view

Iso view

Pattern 02 // Top view

Side view

Iso view

Pattern 03 // Top view

Side view

Iso view

Pattern 04 // Top view

Side view

Iso view

284 Intelligent Patterns

Iso view

Side view

Top view // Pattern 05

Iso view

Side view

Top view // Pattern 06

Iso view

Side view

Top view // Pattern 07

Iso view

Side view

Top view // Pattern 08

Intelligent Patterns

285

Pattern 09 // Top view

Side view

Iso view

Pattern 10 // Top view

Side view

Iso view

Pattern 11 // Top view

Side view

Iso view

Pattern 12 // Top view

Side view

Iso view

286 Intelligent Patterns

Iso view

Side view

Top view // Pattern 13

Iso view

Side view

Top view // Pattern 14

Iso view

Side view

Top view // Pattern 15

Iso view

Side view

Top view // Pattern 16

Intelligent Patterns

287

Pattern 17 // Top view

Side view

Iso view

Pattern 18 // Top view

Side view

Iso view

Pattern 19 // Top view

Side view

Iso view

Pattern 20 // Top view

Side view

Iso view

288 Intelligent Patterns

Iso view

Side view

Top view // Pattern 21

Iso view

Side view

Top view // Pattern 22

Iso view

Side view

Top view // Pattern 23

Iso view

Side view

Top view // Pattern 24

Intelligent Patterns

289

Pattern 25 // Top view

Side view

Iso view

Pattern 26 // Top view

Side view

Iso view

Pattern 27 // Top view

Side view

Iso view

Pattern 28 // Top view

Side view

Iso view

290 Intelligent Patterns

Iso view

Side view

Top view // Pattern 29

Iso view

Side view

Top view // Pattern 30

Iso view

Side view

Top view // Pattern 31

Iso view

Side view

Top view // Pattern 32

Intelligent Patterns

291

Pattern 33 // Top view

Side view

Iso view

Pattern 34 // Top view

Side view

Iso view

Pattern 35 // Top view

Side view

Iso view

Pattern 36 // Top view

Side view

Iso view

292 Intelligent Patterns

We took 4 option to analyze deeper and see which of this possible options have spatial potential. Patterns 04, 05, 17, 25

Iso view

Side view

Top view // Pattern 04

Iso view

Side view

Top view // Pattern 25

Iso view

Side view

Top view // Pattern 05

Iso view

Side view

Top view // Pattern 17

Intelligent Patterns

293

We took 4 option to analyze deeper and see which of this possible options have spatial potential. Pattern 04

294 Intelligent Patterns

Intelligent Patterns

295

Pattern 25

296 Intelligent Patterns

Intelligent Patterns

297

Pattern 05

298 Intelligent Patterns

Intelligent Patterns

299

Pattern 17 SELECTED

300 Intelligent Patterns

Intelligent Patterns

301

Pattern 17 SELECTED G

F D

A Main Curve 1 Main Curve 1 B

B A Main Curve 2

Main Curve 2

Main Curve 3

Main Curve 4

F D

Main Curve 1

Subcurve M3B/B4B

Main Curve 2

Main Curve 3

Main Curve 4

F D

Main Curve 1

A Main Curve 2

A Main Curve 3

Main Curve 3

A Main Curve 4

302 Intelligent Patterns

B Main Curve 4

Pattern 17 connections M1.A -> M2.A M2.A -> M4.A M4.A -> M3.A M3.A -> M1.A Repeat cycle M1.B -> M2.B M2.B -> M4.B M4.B -> M3.B M3.B -> M1.B Repeat cycle etc. Find Middelpoint of created curves Usage of control point curves Connect M1.A -> Midlepoint M1.B/M2.B -> M2.A M1.B -> Midlepoint M1.C/M2.C -> M2.B M2.A -> Midlepoint M2.B/M4.B -> M4.A M2.B -> Midlepoint M2.C/M4.C -> M4.B Repeat cycle ect. Connection of the previously created subcurves Connect Subcurve M1.A/M2.A -> Subcurve M1.B/ M2.B Repeat cycle ect. Loft of the previously created subcurves Connect Subcurve M1.A/M2.A -> Subcurve M3.B/ M4.B Subcurve M1.b/M2.b -> Subcurve M3.C/ M4.C Repeat cycle ect. Connection of the previously created subcurves Connect Subcurve M1.A/M2.A -> Subcurve M1.B/ M2.B Repeat cycle ect.

Intelligent Patterns

303

Pattern 17 SELECTED

Analysis and calculation of the space program required 505,91 m2 281,83 m2 745,41 m2 333,26 m2 329,78 m2 121,33 m2 96,79 m2 37,18 m2 2451, 49 m2 5 arms to fit all the required program

304 Intelligent Patterns

5 4

1 2

TOP view with the 5 arms

Intelligent Patterns

305

07 _

Digital morphogenesis

The final part of the Design Studio focuses on the development of morphologies with Grasshopper to achieve volumetric and spatial response in some way related to the interaction site to system which at the same time establishes a set of unique relationships between site and system, form the analysis. We will define rule to convert in 3D surfaces the poly structures we already have as a system. By applying a new algorithm we will simplify even more the diagrams and make them more comprehensive in terms of form. This new poly surfaces will start defining better the resultant morphologies in the site.

Digital Morphogenesis

307

33.44 6.58

15.56

38.55

The project analysis is simplified into a ONE SIMPLE COMPONENT following the rules connections, and then what the team did was to thoroughly analyze the arm and especially the component(spatially and constructively).

308 Digital Morphogenesis

5 4

1 2

Top view / Render 5 arms Digital Morphogenesis

309

TOP view with the 5 arms

310 Digital Morphogenesis

We analyze 1 arm / 1 arm = 1 component

Digital Morphogenesis

311

Evolution of Component after Workshop â&#x20AC;&#x153;Hibrid Prototypesâ&#x20AC;? by Marco Verde.

Side view / Component

Before

Side view / Component

Component Iso view

Top view / Component

312 Digital Morphogenesis

Evolution of Component after Workshop â&#x20AC;&#x153;Hibrid Prototypesâ&#x20AC;? by Marco Verde.

Side view / Component

After

Side view / Component

Component Iso view

Top view / Component

Digital Morphogenesis

313

Assembly process of Ribs

Component Iso view

314 Digital Morphogenesis

Component + Ribs Iso view

Digital Morphogenesis

315

Closing Catalog

316 Digital Morphogenesis

Pattern 01

Pattern 02

Pattern 03

Pattern 04

Pattern 05

Pattern 06

Pattern 07

Pattern 08

Pattern 09

Pattern 10

Pattern 11

Pattern 12

Pattern 13

Pattern 14

Pattern 15 Digital Morphogenesis

317

Pattern 16

Pattern 17

Pattern 18

Pattern 19

Pattern 21

Pattern 22

Pattern 23

Pattern 24

Pattern 26

318 Digital Morphogenesis

Pattern 27

Pattern 28

Pattern 29

Pattern 20

Pattern 25

Pattern 30

Pattern 24 Subcurve G

Subcurve F

Subcurve E

Subcurve D

Subcurve C

Subcurve B

Subcurve A

Main Curve 1

Main Curve 2

Division of main curves into 8 points:

Connect:

Division of subcurves into 8 points

Point M1 -> A4 -> B5 -> M2

Point M9 -> G7 -> F5 -> E1

Various experiments of connecting points

Point M2 -> B3 -> C1

Point G9 -> F7 -> E9

Fitness Criteria

Point B4 -> C2 -> D1

Point F6 -> E2 -> D1

- Tectonically language

Point B4 -> C7 -> D9

Point F6 -> E8 -> D9

- Light admission

Mirror set of connections

- Structural capacity

Digital Morphogenesis

319

Perspective view

320 Digital Morphogenesis

Top view

Digital Morphogenesis

321

08 _

Prototype

The evolution and development of the prototypes were made in the Workshop of Hybrid Prototype, the team used the software RhinoCAM and Rhinoceros. The team used two types of machines a machine CNC milling machine and a laser machine.

Prototype

323

In this phase of digital fabrication the team chooses two different ways to fabricate the models, the CNC machine and the LASER machine. The CNC machine for milling the component using special software RhinoCAM and for the ribs we used the laser machine. Two different ways to fabricate.

RhinoCam simulator

324 Prototype

Prototype

325

Front view PROTOTYPE

Side view PROTOTYPE

326 Prototype

Side view PROTOTYPE

Back view PROTOTYPE

Prototype

327

In this phase of digital fabrication the team choose two different way to fabricate the models, the CNC machine and the LASER machine. The CNC machine for milling the component using a special software RhinoCAM and for the ribs we used the laser machine. Two different ways to fabricate.

328 Prototype

LASER machine wood material

Prototype

329

Top view // Wood Ribs

330 Prototype

Wood Ribs + Composite

Prototype

331

332 Prototype

Wood Ribs

Prototype

333

Side view MODEL + RIBS // FINAL PROTOTYPE

Side view FINAL PROTOTYPE

334 Prototype

Top view FINAL PROTOTYPE

Iso view FINAL PROTOTYPE

Prototype

335

09 _

Architecture response

The final part of the course. It will be essential to further implement the strategies and tools for design and production processes learned so far. There shall be assigned, a program and a specific place. During the course we must realize that everything is relevant, both workshops and seminars will serve to develop the final investigation.

Architecture Response

337

Program // top view

338 Architecture Response

Program requirement: 500m2: Studio Single space. 500m2: Sheltered housing. 1000m2: Two bedroom apartment. 1000m2: Three bedroom apartment. 1000m2: Loft. 1000m2: Auditorium / Two spaces. 1000m2: Renting work studios. 1000m2: Renting workshops.

Program assembly

300m2: Music essay rooms. 300m2: Exhibition area. 100m2: Meeting rooms. 1000m2: Polyvalent space for neighborhood. 200m2: Bar and Canteen. 100m2: Hall. 300m2: Kindergarten.

Architecture Response

339

Urban / Top view

340 Architecture Response

Architecture Response

341

Urban + Project / Top view

342 Architecture Response

Architecture Response

343

Urban / Perspective view

344 Architecture Response

Architecture Response

345

Urban + Project / Perspective view

346 Architecture Response

Architecture Response

347

Urban + Project / Perspective view

348 Architecture Response

Architecture Response

349

Interior Space

350 Architecture Response

Interior Space

Architecture Response

351

Bibliography: Bio Design Laboratory: Steven Johnson: Emergence: The connected lives of ants, brains, cities and software. Penguin books (2001). Steven Strogatz: The emerging science of spontaneous order SYNC. Michael Hensel, Achim Menges: Morpho-Ecologies. Architectural Association and the autors (2006). Jordi Truco: Para-Site. Time based formation through material intelligence. ACTAR (2011). Adrian Beykers, Ed Van Hinte: Lightness. The inevitable renaissance of minimum energy structures (2001). Achim Menges: Techniques and technologies in morphogenetic design. Wiley-Academy (2006). Michael Weinstock: Techniques and technologies in morphogenesis design. Wiley-Academy (2006). Julian Vincent: Institute for lightweight structures, University of Stuttgart (1974). Michael Weinstock: Emergence-Morphogenetic strategies for design. Wiley-Academy (2004). Michael Hensel: Versatility and vicissitude. Wiley-Academy (2009). Peter Pearce: Structure in nature is a strategy for design. The MIT Press. Cambridge, Messachusets and London, England (1992). Alfred North Whitehead: The concept of nature. Dover publications (2005). Julian Vincent: Smart by nature-lightness. Julian Vincent: Structural biomaterials. Princeton University Press (1995). Christopher Alexander: Notes on the synthesis of form. Harvard University Press, Cambridge, Massachusetts. Christopher Alexander: A pattern language. Oxford University Press, New York. Michael Weinstock: Artifacts and instruments. Architecture Association London (1998). Lionel March/Philip Stedman: The geometry of environment. R. Buckminster Fuller: Your private sky. Lars muller publishers. Adriaan Beukers, Ed Van Hinte: Lightness, The inevitable renaissance of minimum energy structures (2001). J.E. Gordon: Structures, Or why the thinks donâ&#x20AC;&#x2122;t fall. Penguin books, London (1991). A. Quarmby: Materiales plasticos y arquitectura experimental. Editorial Gustavo gili, Barcelona (1976). Renzo Zorzi, Maria Bottero: Zodiac 22, Light structures. Edizioni di comunita (1973). Aranda-Lasch: Tooling, Pamphlet architecture 27. Princeton architectural Press (2006). Frei Otto: Air hall handbook. Stuttgart: Institute for light weight structures. Knaack Ulrick, Tilmann Klein and Marcel Bilow: Deflatables, Rotterdam (2008). 354 Bibliography

Computational Design Laboratory: Douglas Hofstadter: Godel, Escher, Bach: An eternal golden braid. Vintage books. New York (1989). Terry Wino & Fernando Flores: Understading computers and computers: a new foundation for design (1987). Roger Penrose: La nueva mente del emperador. Grijalbo-Mondadori (1991). Alan Turing: On computable numbers, with an application to the Entscheidungs problem (1936). Rudolf Wittkower: Architectural principle in the age of humanism. Random House (1962). Colin Rowe: Las matematicas de la vivienda ideal (1999). William J. Mitchell: The logic of architecture. Design, computer and cognition (1994). George Stiny: Introduction to shape and shape grammars (1980). Peter Eisenman: El fin del clasico. El fin del comienzo, el fin del fin (1984). Greg Lynn: New variations on the Rowe complex (ANY Magazine No. 7/8). Roger Paez: Querido publico: Cartografias operativas y mapas de comportamiento. Roger Bernat Ed. Cendeac. John Frazer: Las ciudades invisibles. The groningen experiment. Fisuras 5. Greg Lynn: Animated Form. Princeton architectural press. Sigfrid Giedion: Mechanization takes command. Oxford University Press (1948). Ali Rahim: AD, Contemporary techniques in architecture. Willy academy (2002). Neil Leach: AD, Digital cities. Willy academy (2009). Reiser Umemoto: Atlas of nouvel techtonics. Princeton Architecture Press (2006). Bernard Cache: Earth moves, the furnishing of territories. Writing architecture series. Alfred North Whitehead: The concept of nature. Dover publications (2005). J.B. Kennedy: Space, time, and Einstein. Acumen (2003). Marinetti, F.T.: Manifiestos y textos futuristas. Ediciones del cotal, Barcelona (1978). Marta Braun: Picturing time, the work of Etienne - Jules Marey. University of Chicago Press. Edward Tufte: Envisioning information. Graphics Press.

Bibliography

355