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AUTOMATION & PROJECTION CARSON RUSSELL MS_AAD COLUMBIA UNIVERSITY GSAPP SELECTED WORKS - 2012 - 2013


INTRODUCTION I believe in design computation. I believe that design computation has a valuable and potentially inseparable role in nearly any architectural design methodology. Computers are good at many things, but they are, in the end, stupid. Any dystopian notion that suggests that optimization software will soon have us out of a job has clearly never tried to account for all the variables that go into any architectural intervention. Or perhaps they’ve never tried to compose a system that holistically accounts for all potential inputs to a specific problem. Computers are good at math. Computers are good at repetition. Computers are good at remembering things. Computers aren’t, however, good at qualitative analysis, pattern recognition, or contextualization. An example of the disparity between these strengths and weaknesses can be seen in our current technologies’ inability to effectively perform multi-sequence alignments in RNA and DNA. DNA and RNA sequences are a completely quantitative set of units, and therefore easily read by computers. That being said, all the brute-processing power that the world’s fastest supercomputers have to offer still is insufficient to produce enough iterations to calculate solutions to this complex problem. It’s a different kind of critical analysis that’s needed to align DNA and RNA sequences, a kind of analysis that comes more naturally to humans. This discovery has led to a multitude of efforts aimed at using humans as a kind of super-computer: interfacing computation with human intelligence through an on-line puzzlegame format. Foldit, Phylo, and eteRNA are just a few examples of online-games that are looking to capitalize on our love of pattern recognition for the advancement of medical science. What this means is that the value isn’t in either system by itself, but in the way that we interface with computers that make them valuable to the design process. It’s about recognizing moments in a design methodology that could beneficially be manipulated by computational strategies. Typically, these kinds of involvements fall into one of two categories.


The implementations in the first category are usually tasks that we (as designers) are capable of achieving manually, but choose to automate in the interest of being alleviated of the monotony to focus on some other, perhaps more integral, part of the design process. This kind of computation operates something like a jig, making stairs and handrails from a plane, dividing a facade into panels, rotating those panels to the tangent of the sun angle on the summer solstice. The implementations in the second category are far more interesting (and potentially more valuable)- computational strategies that perform taks that we are unable to do manually (within reason). Systems that do massive numbers of calculations over and over again. These are computational strategies that project our ideas further than we can naturally predict (or sometimes comprehend). These kinds of implementations often involve the designer creating a system defined by a set of rules or boundaries. They are a general set of instructions by which the system operates. Frequently these are very simple set of rules that allow for wildly variable outcomes. Any miniscule ripple in the base system might grow into a huge wave in the outcome. Instead of designing a chair, and then prescriptively automating the production of it, we design rules that describe a surface to sit and a structure to support it, and let the system explore the extremes of the constraings we’ve provided. I came to Columbia’s GSAPP to further my intoxication with design computation : to push the envelope of systemic logic; to design frameworks that produce outcomes just beyond my realm of my expectations. I believe in the responsible use computational and parametric design methodologies as an integral and relevant part of the design process, and have tried to explore what it means to design in this way throughout my time at the GSAPP. My work over the last 3 semesters is loosely organized into the two aforementioned categories: automation and projection.


explorations 06__A Caroรงo ADV. STUDIO III // SPRING 2013 40__Magmatic Contingencies ADV. STUDIO II // FALL 2012 52__S.C.C.S. ADV. STUDIO I // SUMMER 2012 66__Evolutionary Boids VISUAL STUDY // FALL 2012 86__Composite Optimization SEMINAR // SPRING 2013 86__Petrification VISUAL STUDY // FALL 2012 90__Adaptive Forms VISUAL STUDY // FALL 2012 98__S.F.R.R. VISUAL STUDY // SUMMER 2012


A CAROCO

ADV. STUDIO

A CAROCO SOFTWARE: RHINOCEROS 5 GRASSHOPPER SOFTWARE: CNC MILL

A CAROCO, or the Seed in Portogese is an intervention developed for Mario Gooden’s Urban Futures Pt:II : Digital City studio. The Seed is a composition of program in dialogue with ideas of lightness and darkness, tangibility and intangibility, scale, material, and emergent technology. The Seed is initially inspired by the massive Brazilian agriculture industry, its direct impact on the Brazilian economy, and the visibility (or invisibility) of this within the day to day culture present in Rio de Janeiro. These ideas are explored through the medium of a mediateque in Rio de Janeiro.

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SPRING 2013


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A CAROCO

ADV. STUDIO

INITIAL RESEARCH AGRICULTURAL PRODUCTION

Brazilian Production, 2010 CATTLE COFFEE CORN RICE SUGARCANE SOYBEANS WHEAT

LINE OF ADJACENCY BAHIA SERGIPE ALAGOAS PEMAMBUCO PARAIBA RIO GRANDE do NORTE CEARA PIAUI TOCANTINS MARANHAO AMAPA PARA RORAIMA AMAZONAS ACRE RONDONIA MATO GROSSO GOIAS DISTRICTO FEDERAL MINAS GERAIS ESPIRITO SANTO RIO de JANEIRO SAO PAULO MATO GROSSO do SUL PARANA SANTA CATARINA RIO GRANDE do SUL

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The analysis of each crop is evaluated on a state-by state basis through the years of 1931 through 2010. The data breaks down into three values per year per state- hectares planted, millions of Tonnes of yield, and millions of R$ earned.

1_Hectacres planted

2_Millions of Tonnes of yield 3_Millions of R$ earned

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

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A CAROCO

ADV. STUDIO

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MAGMATIC CONTINGENCIES

ADV. STUDIO

MAGMATIC CONTINGENCIES SOFTWARE: REALFLOW RHINOCEROS PYTHON GRASSHOPPER RHINOSCRIPT 3DS MAX AMIRA

Magmatic Contingencies was developed with Mengna Miao as a project for Francois Roche’s (n)certainties studio. Magmatic Contingencies is an exploration of a kind of battle of impermanency. A swarm of robots inhabit a volcanically active icelandic glacier and spray super-cooled water into the advancing magmatic field. The result is a delicate and charred latticework evident of the struggle between the two extremes.

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MAGMATIC CONTINGENCIES

ADV. STUDIO

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MAGMATIC CONTINGENCIES

ADV. STUDIO

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MAGMATIC CONTINGENCIES

ADV. STUDIO

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MAGMATIC CONTINGENCIES

ADV. STUDIO

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FALL 2012


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PROJECT CONTINGENCIES MAGMATIC

COURSE ADV. STUDIO

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SEMESTER FALL 2012


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SCCS

ADV. STUDIO

SUMMER 2012

SELF-CONTAINED COMMUNITY SPONGE SOFTWARE: RHINOCEROS GRASSHOPPER

In the year 2050, planet Earth is home to 9.2 Billion people, and is becoming one large megalopolis. Urban density has increased by 50% in cities and the infrastructure is unable to sustain the vast increase in population. As the population explodes, potable water and sanitation systems struggle to keep up in developing countries. What if we could harness this rapid population growth, and use it to fuel new architectural typologies aimed at alleviating the issues involved with rapid population growth in 2050? What if the super-abundant urban activity could be harnessed and grown vertically to create a super-dense infrastructure capable of exploring new spatial potentials? What if this infrastructure could serve to nurture a community during peacetime, and protect it during periods of conflict? What if this infrastructure could articulate to become applicable not only in developing countries, but in any context? The Self-Contained-Community-Sponge, or SCCS is a deployable infrastructure aimed at improving the ability of an urban context to support it citizens. It uses urban, cultural, environmental, and spatial cues to work as a kind of catalyst to spark architectural invention. By providing an incubating environment for an existing urban context, the community is allowed to thicken and fuse with the system to spark new architectural potentials. In its most rudimentary format, the sponge consists of an elevated platform that serves as an armature to shelter an urban context. The elevated landscape provides a water catchment and filtration terrain while simultaneously providing a dynamic park space. This terrain that has the potential to infect the context below to inspire a new architectural order. The floating armature works as a kind sponge that absorbs and filters water, but more importantly one that soaks up and redistributes urban energy. SCCS can work in any climate, whether climate refers to a set of ecological and atmospheric conditions, or whether climate refers to a cultural and social order. In each of these different climates, SCRCS manifests in different ways, each time evolving to suit the specific needs of its given location while still presenting with a recognizable topology. A deployment in rural North Carolina may articulate more towards youth recreation facilities, a recapturing of an old textile mill, and hydro-electric energy generation, while a deployment in Monrovia, Liberia may need a superstructure capable of sheltering the street from the suns searing heat, passive cooling structures, and medical facilities for refugees fleeing from persecution. SCCS evaluates the climatic, social, cultural, and urban context of a site in service of generating a nurturing infrastructure that can only be conceived of at the collision of complex social, economic, and climactic cues.

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SUMMER 2012


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SCCS

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SCCS

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SCCS

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PROJECT GENETICS I

COURSE

SEMESTER

community welfare

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t wa

c er

on

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io at v er

rs

up

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po

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n co

ge

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n ti o

d is

io rs pe

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filtration surfaces

water catchment

Circulation

Pas si

ve c

ool

ing

THERMAL CHIMNEY

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SCCS

ADV. STUDIO

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SUMMER 2012


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EVOLUTIONARY BOIDS

SWARM INTELLIGENCE

E. BOIDS SOFTWARE: PROCESSING RHINOCEROS GRASSHOPPER AMIRA

Evolutionary Boids seeks to generate emergent behavior in a basic boid structure through the use of a genetic algorithm. The project is spit into two attempts to solve the problem, in the first half, the evolution is driven through interactions between the boids on a local level, so the process isn’t stepped, but rather dynamic. In the second half of the exploration, the evolutionary bridge, a global system of evaluation is developed wherein the entire system evolves over discreet generations.

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ALIGNMENT

2

1

Alignment defines each boids desire to travel in the same direction as its neighbors.

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Boid [X] reads the current direction of all boids within the given range[1, 2, 3], averages those directions, and changes its own current direction towards the averaged result.

x

SEPARATION Separation defines each boids desire to keep a minimum distance from neighboring boids 3

Boid [X] reads the current distances of all boids within the given range[1, 2, 3], and assumes a vector from each neighbor to itself. It averages those vectors and moves in that direction.

2

1 x

COHESION Cohesion defines each boids desire to group together with neighboring boids. Boid [X] reads the current positions of all boids within the given range[1, 2, 3], and averages those positions [+]. It creates a vector betwen itself and that position, and moves along it.

3 2 1

x

OCHLOPHOBIA To keep the boids from completely homogenizing, each boid is instilled with ochlophobia- the fear of large groups. If a boid sees that there are too many neighbors in its group, it becomes ‘anxious’ and inverts its trajectory.

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5 7 x

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SEED IMAGE Jackson Pollock: One: Number 31, 1950 A seed image is used to generate the colors for each of the boids. Upon creation, the image is randomly sampled, and the color chosen defines each boids identity.

seed color

(222, 206, 155) GENETICS Each boid has its own unique color. This color determines the boids ‘genes’ The brightness determines its size- brighter = smaller The brightness determines its speed- brighter = faster

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INPUT Each boid reads the input from its primary behaviors, and scales down the results.

3 2 1

x

RAW INPUTS

SCALED INPUTS

TYPICAL SCALING Usually, each boid interprets each behavior equally, the result is a balanced boid.

alignment x 0.87 separation x 0.80 cohesion x 0.60

GENETIC SCALING With genetic scaling, each boid is instilled with its own genetic code by scaling each behavior differently, as perscribed by its seed color.

alignment x 1.00 separation x 1.00 cohesion x 1.00

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seed color

(222, 206, 155) 222/255 = 0.87 206/255 = 0.80 155/255 = 0.60


REPRODUCTION 2

Boids are given the opportunity to breed with a neighboring boid if a number of conditions are met:

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1. Group size must be within given range: must be larger than 3 but less than 20

1 x

2. Sufficient time must have passed since last mating: each boid can only reproduce at a given interval

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FINDING A SUITOR 2

If reproduction conditions are met, the boid begins to search for a suitor.

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All boids are androgenous, but the one doing the scanning is referred to as the ‘Mother’ and the suitor is the ‘Father’

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3 1 x

The closest boid within the group becomes the suitor.

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neighbor #3

mother

father 3

(129, 110, 104)

129 110 104

GENE CROSSOVER

x

(222, 206, 155)

child

222 206 155

Both parents have the chance to transfer genes to the child. Each gene is randomly chosen to be from the mother or father. The child receives 1 gene for each attribute.

(129, 206, 104)

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PROJECT

COURSE

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SEMESTER


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TRANSLATION TO 3D In the initial script, the drawings are constructed through the accumulation of ellipses drawn in 2D space, the only “intelligent” objects were the agents themselves. In moving to 3D space, the evolutionary boid script was reorganized and combined with an ant-trail algorithm to produce 3 distinct objects: the Pilot, the Strand, and the Agent. //THE PILOT //THE STRAND

THE PILOT

//THE AGENT

Functions like the ‘agent’ of the evolutionary boid script, exhibits the basic swarm behaviors of alignment, cohesion, and separation. Each pilot is seeded with a color from an initial source image which becomes its “DNA”

THE AGENT Agents are produced by the agent, left behind the pilot as a kind of trail of breadcrumbs. The Agents exhibit two basic behaviors- strand cohesion and spring force.

THE STRAND Strands are the organizational structure that house the Agents, they are formed by the pilot and contain the DNA for the agents.

STRAND COHESION As the Pilots have a behavior to cohere amongst themselves, so do the agents along each strand. Strand Cohesion defines each agents desire to group together with neighboring boids on different strands. Agent [X] reads the current positions of all other agents [on different strands] within the given range, and averages those positions [+]. It creates a vector betwen itself and that position, and moves along it.

SPRING FORCE Spring Force describes each agents desire to straighten out its own strand. Each Agent along the strand locates its adjacent neighbors in its own strand and calculates a vector that averages the two together. The result is a vector that leads to the alignment of the agents on the strand.

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BASIC BRIDGE MORPHOLOGY The goal of this exploration is to define a system that allows for evolutionary change to drive an emergent system of optimization. In its most rudimentary form, the bridge consists of a population of ‘biologically diverse’ pilots (seeded by a painting) that gradually seek a target at a location that serves as the end of the bridge.

EVALUATION Each bridge is grown for a given interval (usually 1500 frames) and then evaluated for success. Each pilot within the system is currently evaluated on two basic criteria: 1. How close it got to the end of the bridge 2. How many unique connections its strand made with other strands From these two criteria, a fitness value is calculated (between 0.0 and 1.0). At the end of each cycle, the fitness value is used in determining who will compose the surviving population (generally the top 33%).

REBIRTH The surviving population is used in the production of the next generation. Each of the survivors is “bred” with a random suitor (from the list of other survivors) to produce a gene set (R,G,B values) that is more successful than the last. These hybrid colors are then used in the generation of the new population of pilots.

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EVOLUTIONARY BOIDS EVOLUTIONARY BRIDGE

SWARM INTELLIGENCE

MUTATION A

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FALL 2012


MUTATION B

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EVOLUTIONARY BOIDS EVOLUTIONARY BRIDGE

SWARM INTELLIGENCE

SECTIONS THROUGH MUTATION C

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EVOLUTIONARY BOIDS EVOLUTIONARY BRIDGE

SWARM INTELLIGENCE

MUTATION A

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FALL 2012


MUTATION B

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EVOLUTIONARY BOIDS EVOLUTIONARY BRIDGE

SWARM INTELLIGENCE

MUTATION A

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FALL 2012


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EVOLUTIONARY BOIDS EVOLUTIONARY BRIDGE

SWARM INTELLIGENCE

MUTATION C

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FALL 2012


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PETRIFICATION

ENCODED MATTER

PETRIFICATION SOFTWARE: REAL FLOW RHINOCEROS RHINOSCRIPT GRASSHOPPER AMIRA

Petrification was produced alongside Magmatic Contingencies (with Mengna Miao). The goal of the mandatory digital workshop was to develop computational tools to work influence and benefit Francois Roche’s (n)certainties studio. Petrification uses a similar workflow as Magmatic Contingencies in pursuit of a tool capable of designing with extreme temperature fluid dynamics.

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FALL 2012


MORPHOLOGICAL CATALOGUE

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PETRIFICATION FINAL RENDERINGS

ENCODED MATTER

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ADAPTIVE FORMULATIONS

ADAPTIVE FORMULATIONS

PEDESTRIAN BRIDGE SOFTWARE: CATIA

Adaptive Formulations focused on highlighting the value of CATIA as a powerful design and workflow tool. This 300’ pedestrian bridge explores various computational modeling solutions within the CATIA environment while maintaining the elegance of a swiss pedestrian bridge.

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PROJECT GENETICS I

COURSE

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SEMESTER


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ADAPTIVE FORMULATIONS

ADAPTIVE FORMULATIONS

FACADE FOR A PARKING GARAGE SOFTWARE: CATIA

The garage facade uses CATIA as a modeling solution to flex parametric constrains not achievable in other software. The base unit of the entire system is a ‘blade’. Each blade is instantiated through a knowledge pattern along two sets of points housed on a larger frame. The points are instantiated along the top and bottom chords of the frame (via knowledge pattern) as a product of the distance between the center of the frame and a given attractor curve). The closer to the attractor curve, the more points, and subsequently, the more blades. This entire assembly (frame + points + attached blades) is combined into a single PowerCopy that is hand instantiated along a grid of points defined by the structural grid of the garage. This grid of points is distorted as a function of the distance between a given point and a control surface. There is a user-defined parameter called Threshold. If the distance between the grid point and the control surface is greater than the threshold, the point moves upwards, otherwise, the point moves downwards. Comprehensively, the facade system layers several parametric component systems to achieve a highly adaptable and responsive facade system.

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PROJECT GENETICS I

COURSE

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SEMESTER


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SHELTER FOR ROMAN RUINS

DIGITAL CRAFT

SHELTER FOR ROMAN RUINS EQUIPMENT: NIKON D90 UNIVERSAL LASER CUTTER SOFTWARE: RHINOCEROS GRASSHOPPER 3DS MAX AFTER EFFECTS

The aim of this series of assignments was to use a built architectural work (preferably one completed since 1990) as a vehicle for learning a computational tool-set. Throughout the semester, the precedent was consistently developed- beginning with a rudimentary set of drawings, a 3D and physical model was constructed. The concluding assignment was to represent the digital model through a series of renderings and a short (30 second) rendered video. A series of grasshopper scripts were deployed to digitally construct the exterior louver system and the tension cabling of the trusses.

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SHELTER FOR ROMAN RUINS DETAILS

DIGITAL CRAFT

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SUMMER 2012


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SHELTER FOR ROMAN RUINS MEASURED DRAWINGS

DIGITAL CRAFT

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SHELTER FOR ROMAN RUINS PHYSICAL MODEL

DIGITAL CRAFT

104

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SHELTER FOR ROMAN RUINS AXONOMETRIC

DIGITAL CRAFT

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SUMMER 2012


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SHELTER FOR ROMAN RUINS FINAL RENDERING

DIGITAL CRAFT

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SUMMER 2012


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PROJECT

COURSE

110

SEMESTER


Automation & Projection