NEGOTIABLE SPACES

NEGOTIABLE SPACES Ramon G. Pena Toledo Thesis

Advisor: Ezio Blazetti Ramon G. Pena Toledo Thesis Studio 2016 University of Pennsylvania School of Design Arch-706

Acknowledgements First and foremost, I would like to give thanks to my thesis advisor Ezio Blazetti for his insightful feedback in the development of this thesis, both as a masterful critic and a friend. I would also like to thank Annette Fierro for her help and guidance throughout the entirety of the thesis program.

To my thesis colleagues, I owe you a special gratitude. You helped me at my best and my worst and if anything in this body of work is to stand out, it most definitely has your touch.

To my family, thank you. I’m only here because of all the good decisions, hard work and sacrifices you’ve made throughout all my life.

I’ll always be grateful for you.

Thesis Question To what Extent, can Interactive Architecture learn to interpret human generated data so as to include users in the negotiation of their environment?

To what Extent

Exposes the key axiom that Interactive Architecture “IS� able to interpret Human generated Data and manipulate it to achieve spatial outcomes.

Interactive Architecture

Specifies a particular sub-field of the discipline of Architecture under which the project operates. Responding to its particular history, terminology and discourse.

Learn to interpret

Formally Models and Analyses data pertaining to the physical world through the lens of techniques adopted from Machine Learning.

Human Data

Live Data resulting from people’s position and programmatic use of a space

Negotiation of Environment

Fostering a conversation revolving around the desire to fulfill multiple programmatic and spatial configurations within a limited area. Engaging this process as it changes with time.

Abstract Part 1 - Interactive Architecture The current state of interactive architecture today and its discourse.

Part 2 - Relevance of Predictive Algorithms Machine learning that aims for the “negotiated� fitness of a spatial solution Part 3 - Methodology Expands the inquiry to the site

Within the context of the information age we have seen the emergence of an endless, and now expected, stream of disruptive technologies and intelligent networks. They propose a new rhetoric towards our physical reality which is not rooted in novelty but in the offering of new affordances and abilities.

Abilities that allow us to understand and study what was previously considered as negative space within our knowledge culture. This is the ethos of data-mining. The continuous monitoring of uncomfortable amounts of information, mined either directly or indirectly from us, sparks a renewed interest for new models through which to study human behavior.

Standing on the inclination that there are sunken patterns lying beneath a broad sea of observational data. These attempts are simultaneously heralded by an ever growing library of decision-making and predictive algorithms capable of processing vast quantities of meta-data and conclude structured inferences and models of collective behavior. It is within this reality that designers face a need to represent the inner workings and nuances of what would otherwise be seen as the algorithmic black boxes that control and operate our spaces at an ever increasing pace.

The goal of this thesis is to deal with the representation of a form of crowdsourced architecture. One that through means of actuators is able to change its geometry and operation based on feedback from a collection of users and thus raises the question of how to design such multi faceted spaces.

Through shifts in geometry, can architecture be negotiated in real time? How do we manage multiple interests in one urban site?

What are the computational methods needed to regulate and operate an architecture that is always in motion?

We hypothesize that if architecture were to instill, within its spatial medium, the intelligence to interpret the data it generates from its users it could be able to produce spatial repercussions in real time that are synchronous with our collective metrics of value. A special focus will be placed in the methods that enable interactive architecture to serve as a medium for communication, in real time to reveal hidden socio/cultural polemics in the urban environment.

Interactive Architecture Part 1

Initial Conceptions of Interactive Architecture Typologies in Interactive Architecture: Two Classes Tropes of Interactivity Organizational Model

Initial Conceptions of Interactive Architecture The Thousand Dreams of Stellavista, JG Ballard 1962 The first idea that we would like to do without, within the field of interactive architecture, should be the notion that adaptive environments are in any way new. One of the first to speculate about the potential and limits of autonomous environments was JG Ballard as evidenced in his compilation of stories “Vermilion Sands”. In the book we find “The Thousand Dreams of Stellavista”, a short sci-fi fiction exploring the relationship between humans and an architecture with its own potential for personality. The premise of the story sits in a near future where architecture is able to sense and store the personalities of its occupants in order to react to them in autonomous fashions.

These Psychotropic houses rely on two things; an array of sensor cells that sense and record the inhabitants actions, moods, tempers, habits and a multifunctional material called Plastex that is able to change its shape and texture at will. The antagonist of the story, a house named Stellavista 99, was the stage of a cold blooded murder under its previous owners. As a new couple comes into the residence, they see themselves both physically and emotionally affected by the projected moods and memories of the previous owners, to the point where the architecture manages to split the young marriage and in the culmination scene even attempts to reenact the murder scene of its previous tenants.

There are a couple of arguments coming into light here. Evidently there is the question of whether architecture should ever possess the ability to have its own autonomy. In the story the house had the ability to store personalities. The house never constructed its own personality though, instead it was a cocktail of responses accumulated by living and nonliving tenants stored up in its memory. In this sense the intelligent environment is but an extension of the characters who lived in it. This can be seen as the exaggeration of anthropomorphism in architecture. Ballard expresses here a sort of impossibility for the intelligent environment to posses a truly autonomous nature for itself.

This is because autonomous behavior is only understandable within a human context sentient enough to recognize its presence. Hence, behavior is here understood to be relational, involving two people or more, even if displaced by time, gender or even death. The architecture is certainly a vehicle for communication, and a medium for communication cannot be static in nature, it is necessary for it to dynamically express information.

“An unfortunate aspect of psychotropic houses is the factor of resonance - diametrically opposed personalities soon stabilize their relationship, the echo inevitably yielding to the new source. But where the personalities are of similar frequency and amplitude they mutually reinforce themselves, each adapting itself for comfort to the personality of the other.”1

1

“The Thousand Dreams of Stellavista” - J.G. Ballard (1962)

Initial Conceptions of Interactive Architecture Gordon Pask 1969 This notion of a physical artefact that enables communication was taken upon by the cyberneticist Gordon Pask. While his main interest was to define the architecture of a converstation2, part of his research went dedicated to the creation of prototypes and machines, that fostered the yet unexplored intersection between computer and human communication. One of his most quoted inventions, the “Musicolour Machine” serves as a powerful example of interaction design.

The mission of Musicolour machine as described by Andrew Pickering2 was very simple, an apparatus that had the ability to control a light show based on the musical output of a performer. The way in which it worked , though simple enough to fit in a few lines of code today, was both entirely analog and state of the art for its day. Materially, the music was converted into an electrical signal via a microphone. Within Musicolour though, the signal passed on through a set of filters, sensitive to different frequencies, the beat of the music and other nuisances, all in all to create the electrical output that was then fed to multiple lights of different colors. It was not the task it did that was of importance for Pask, it was the way in which it achieved it. In essence the ingenuity of the machine relied on the fact that it was programmed to get “BORED”.

In order for the machine to obtain the embedded intelligence of getting “BORED” it had to address, within its circuitry, a model of what is deemed pleasurable or undesirable.

For example, after a period of time playing, the performer was struck with a wide range of unpredictable light patterns and color fluctuations that were incongruent with the pace and tone of his music. Thus in order to end the rather undesirable effect of edm-like strobe lights paired against a mellow sonata, the performer was forced to alter what he was playing in order to regain back a sense of stability.

Essentially what we see here is a coexistence between performer and machine that produces a product that is neither controlled by human or machine logic yet one that intentionally designed to be neither and both. This was the synergistic effect that Gordon Pask wanted to achieve in the end. Though he did not explicitly mention ever to what end, this was the way he introduced his conversation theory, and to a larger sense the whole field of cybernetics, to what can be considered an architectural product within its own right.

Gordon Pask presenting Musicolour

Musicolour Machine, 1953-57

Colloquy of Mobiles, 1968

“The design goal is nearly always under specified and the “controller” is no longer the authoritarian apparatus which this purely technical name commonly brings to mind. In contrast the controller is an odd mixture of catalyst, crutch, memory and arbiter. These, I believe, are the dispositions a designer should bring to bear upon his work (when he professionally plays the part of a controller) and these are the qualities he should embed in the systems (control systems) which he designs.”2

2

“The architectural relevance of cybernetics” - Gordon Pask (1969A, 496)

Initial Conceptions of Interactive Architecture Moving forward onto the discourse of Interactive Architecture it is important to note John Frazer and Nicholas Negroponte who where both academics opperating under architecture’s intersection with technology.

In a time when CAD programs started to influence the way that architects design, they began to speculate upon the ways we could automate and parametrize architectural solutions. When questioning to what extent can we control the device of automation, Negroponte famously proposed that when a processor is able to “find a method that finds a method” the question of authorship then passes to become a more nuanced subject and it is impossible to separate it from the machines’s doing. In essence he was questioning how the machines can learn about the human processes we wanted to accomplish.

Similarly, John Frazer proposed that if an outcome is said to anticipate results before they are required, the process can be said to have some intelligence embedded in it.

He elaborated more about this in his book “Evolutionary Architecture” where parametric methods for solving architectural morphology were developed. Frazer specified that intelligence can only be seen in the scope of a system that involves entities interacting with

each other, where a fundamental difference can be seen and intelligence itself does not live alone but bounces between discrete networks of individual operators.

Terms for Interactive Architecture

The subject of interaction has been written about extensively but a common line that most proponents share is the fact that interaction implies some form of common feedback. It can be seen in many ways as a two way street, in which that which is being interacted with exchanges feedback with he who interacts. The transfer of information between entities that themselves process and produce such information autonomously.

Responsive Environments,

Hence: Interaction = Feedback

Intelligent Environments,

Smart Architecture, Soft Space (Negroponte), Transactive Intelligence (Marcos Novak), Interactive Architecture (Usman Haque), Evolutionary Architecture (John Frazer), Adaptive-Conditional Architecture (Charles Eastman) Synergistic Effect (Gordon Pask)

“Ironically, an environmental humanism might only be attainable in cooperation with machines that have been thought to be inhuman devices-devices that can intelligently respond to the tiny, individual, constantly changing bits of information that reflect the identity of each urbanite as well as the coherence of the city. “

Our model will derive order from its environment and be controlled by a symbiotic relationship with its inhabitants and that environment.

It knows the coded instructions for its own development and is thus, in a limited sense, conscious. It can anticipate the outcome of its actions and therefore can be said to have some intelligence. All the parts of the model cooperate and in that sense it can be considered as an organism, but it will only fully exist as such if it is a member of an evolving system of organisms interacting with each other as well as with the environments.�

Typologies in Interactive Architecture: Two Classes Linear

Keifer Technik Showroom

RobertDelong, Global Concepts - SoftLab

Museum of Future Government - SoftLab

HypoSurface, MIT media Lab, C.W. Allen Group

Mega Faces Sochi Olympics

Non-Linear

Hylozoic Soil - Philip Beesley

Regardless of how intricate the relationship becomes, a user will after a period of time begin to see the installation as an extension of his or her actions Solves Conflict through Operation

A multilayered object. An architectural object that has multiple ways of responding to stimuli and thus must “interpret” or decide which way to do so in accordance to an explicit “metric of value” Solves Conflict through Interpretation

This definition of interaction allows us to distill the field of interactive or mechanistically reactive architecture into two distinct typologies, which we will classify as Linear and Non-Linear. A linear installation is a type of interactive architecture that responds to our presence but one that always does so in a very predictable fashion. Never changing the way it processes information. In other words, the installation only shifts its state in some unilateral way, evading the problem of conflict altogether, or otherwise disabling itself from being effectively used as a serious tool for communication. The energetic, mechanical and practical benefits fall behind those achieved by carefully designed static structures because,their responsiveness can eventually be seen as an extension of the human body and its actions in space.

Where as a Non-Linear installation, is an architecture that is composed of modules, parts and multiple ways of interacting with you. This means that the architecture has to decide how to respond to you or have some form of intelligence that enables it to decipher how to do so. They are a two-way platform for communication where users can interact with an object, obtain a form of benefit but at the same time the object “INTERPRETS” the conflict of spatial agendas and allows

them to actively coexist with each other.

Here then, through the modularization of discrete architectural components, capable of processing information non-linearly, we could have a Paskian tool for design that would be able to handle heavy cognitive loads in a manner that more static and stable solutions cannot provide. It is the interest of this thesis to figure out ways in which this intelligence can be brought to realization as will be described in the specific design methodology that was explored.

Tropes of Interactivity

N

N

N

N

Consecuence Footprint

Consequence Footprint A Document that accumulates History

Resource Distribution N Y Y

Y

Y

Spatial Scale

Scalar Effort

Y Scalar Effort Obstructs Action, Provides Time for Reconsideration

N

Y N

Y N

N

Y

YN

Y

N

N

Y

Shared Resources

N

N

N

N

Spatial Scale

Shared Resources Enforces Quorum

N Spatial Scale

Y

Y

Resource Distribution

Resource Distribution Efficiency Manager

Spatial Scale

Y

Y

Y

N

Y

Y

N

Y

Nitipak Samsen - Buttons Part of this intelligence was explored through what we call tropes of interactivity.

Provided by Nitipak Samsen in his short video “Buttons”, these strategies were enlightening because they talked about interaction at a basic and fundamental level. The video essentially explores what it means for objects to store history, have a particular inertia to touch and administer resources. The trope of most interest, which we titled “Efficiency Manager” dealt with the interaction with a 6-light switch. Of those 6 lights, only 3 could

be turned on at the same time. Hence, when you flicked on the fourth light the light switch had to itself think and decide which of the previous three lights it would turn off.

Essentially it acted as an efficiency manager, redistributing resources on the fly, a decision maker.

Organizational Model

Local Group Global

In order to achieve a space able to provide multiple responses, we will need to propose a multi layered collection of objects with the ability to be actuated. Luckily for us this is not a new concept for it has been tackled successfully by a range of interactive projects before. Standing out in this line of research is the work of Phillip Beesley’s “Hylozoic Ground” at the Venice Biennial, Italy 2010.

In this case we see a collection of actuators, servos and deployable structures that individually perform very simple tasks, but collectively have the embedded intelligence to respond in a simultaneous fashion towards a desired goal. The project is composed of a multiplicity of parts that move, metabolize and interact with each other. Yet, the particular way in which they react to human presence is through the arms and modules that respond to local stimuli from human movement. At the same time they respond to local groups of neighboring arms and their movements, and at a global level there is a group to group relationship within each other curated by a controller. In this sense, projects like these come close to what we could expect as the output of the thesis’ exploration.

“The distributed system consists of 38 controller boards, all with identical hardware. Specialized functions are assigned by software setups in groups of boards, and one board assumes a supervisory role for the entire system. Individual boards all talk on the bus controller’s receive lines, and listen to the controller’s instruction on the parallel send lines.

Information is transferred from board to board via the controller.”

The Relevance of Predictive Algorithms Part 2

Evaluation & Spatial Organization Neural Networks & Inputs Limits Disciplinary Relevance

Evaluation & Spatial Organization Responsiveness to Pressures

Need

Adaptability throughout Time

Pressure

Not Needs > Instead Pressures

Evaluation

Spatial Output

One concern within this project is the production a model of organization similar to Beesley’s where we can develop the intelligence required to include users in the negotiation of their physical environment.

What we are really responding to is defined here as established pressures instead of particular needs. Christopher Alexander suggested4 that the concept of a form influenced by need, is not inclusive enough and that it is in essence too inactive. Instead, form ought to be influenced by the concept of pressure, which is a set of forces: a series of interacting and non-isolated elements in which a change in any one force affects the result.

Our architecture wants to be able to evaluate these collective pressures and produce some form of spatial product informed by them.

Rather than explicitly explaining why interactive systems are necessary, meaningful, or useful, we state that the motivation to make these systems is found in the desire to create spaces and objects that can meet changing needs with respect to evolving individual, social and environmental demands. This user generated “AGENDA”, can be treated as a data set that represents the physical state of an environment that would best benefit a particular person at a particular time. Our envisioned architecture is one that has the physical means, through modular actuated parts, and computational resources to respond to various demands within limited space.

This is to say that for the scenario in which multiple entities want different spatial outputs from of a limited space, the architecture can provide a means to negotiate between conflicting desires. But, where a single act of negotiation would not suffice, what we want to create is a platform that adopts these pressures as they change throughout time.

The envisioned architecture is one that has the ability accomplish this by rearranging its geometry in multiple ways through the act of kinetic actuation.

Need

Pressure

Evaluation

Spatial Output

4

“From a set of forces to a form” - Christopher Alexander

Neural Networks & Inputs The particular comptutational strategy by which this thesis proposes that such architecture can be operated is through a neural network performing a backwards propagation algorithm. A neural network is a concept developed within machine learning, a subfield of computer science. Machine learning, as Jason Brownlee defines it, is the training of a model from data that generalizes a decision against a performance measure.

The particular characteristic that makes this method so powerful is the way it handles the measurement of its own performance. In this light, Thomas Mitchell famously correlated experience with performance. Stating that if with increased experience, the performance of a particular method improves you can say that your process has learned something.

it has done so, judges how far from the desired overall result it was. It then passes to change the weights attached to each node, determining those that contribute more to the degree of error than others and giving them more freedom until their contribution to the global degree of error is lowered. One could say that neural networks seem to “LEARN� how to solve problems but in reality they just perform an iterative process of adaptation with the goal of reducing the global error stated via their performance. We’ve yet to explore what the application of these processes means to architecture, but it is in our interest as designers to find out. One could see or call this form of endeavor as a step towards the crowdsourcing of architectural solutions in real time.

We should see this method implemented as the proper way in which our architecture mediates ___________ Experience = Performance Measure between users and their, collective forms of Task desired engagement. Neural Networks are a way of training a computer program to solve a particular problem to which you know the solution to. Like their organic counterparts these virtual systems have multiple nodes, which we replace here for actuators, servos, motors, lights and deployable structures and they also include weights which are here interpreted as how much does an individual element move/perform. Initially a neural network adjusts the weights at random and after

Inputs:

Outputs:

Skeleton Tracking

Duration

Rating Feedback

Actuator Movement

Untrained Installation

Installation in Training

Feedback

Trained Installation

Feedback

This process, in order to solve a particular problem needs to be fed a particular set of inputs. It is the way that it models the world in order to compute a solution. The way that we will be gathering this input data, our own assumption of the world, is going to be through direct human input. The particular input are going to be kinect skeleton tracking, which is but a technological means to empirically measure human position in three dimensional space, and the performance measure is going to be actual human feedback that will be fed to the algorithm in the form of a numeric scale, similar to many of the ratings systems used in contemporary online streaming services. The Idea is that, a user will rate the architecture’s performance on a particular task and it will “learn� how to operate under this input.

This is to say that for an untrained installation the architecture is going to behave quite randomly and most certainly under perform in its initial ratings. Yet, what will happen next is that the installation will enter a training phase in which it will be iterating over multiple options and architectural configurations.

During this stage it will determine which specific actuators need to be modified and provide multiple configurations of them until a pattern of successive positive feedback is formed. When you rate it positively the installation will assume that it has learned from you, that for that particular task and that particular set of inputs(human position) it should perform in a determined and discrete particular way.

Neural Networks & Inputs Within this scenario, there will come a point when we will come face to face with the event that two people will debate over oposing or different solutions(feedback) within a similar region. What we want to achieve is an iterative process in which we will find that users will start to communicate through the physicality of the architecture in order to resolve the particular conflict. To a certain degree you can expect the architecture to meet you halfway, but it is through this act that this process will result in a paskian tool for social development because this will force one or more of the players to change or alter their stance on the space. It will make us face the situation that we want different things in our urban environment.

he sees the world determines the which buttons he can press at particular situations. When rewinded back to generation 0 the algorithm is more useless than one would think. It doesn’t even know that pressing the right button makes the player progress through the game.

Still, through a series of iterations of its internal configurations (weights) it manages to press the right button and make some progress in the game. As it does so it measures the fitness which is what machine learning is all about, how much you progress at a certain task. Once we have a robust measure that compares iterations based upon how well they performed we can distill those configurations that proved to be more useful and build upon them to develop an even greater complexity of responses.

Feedback Actuation

The materiality of the particular architectural composition ought to be tensile surfaces because of their ability to express large changes in geometry with little structural effort. Not only that but they also have the ability of being a compatible surface and texture with the humans that are potentially interacting with it.

One studied example of a neural network was MarI/O by Seth Bling, a program that has been trained over time to play the game of mario.

The way that it works is that it deduces the world by simplifying it to places the player can stand and those where he cannot. The way that

MarI/O - Seth Bling

Limits Another limitation that is foreseen is one we define here as the “tyranny of the majority”. In the training phase there is going to be a play between multiple pressures seeking to use the installation in differently and sometimes opposing ways. There may be a situation in which a majority of people skew the architecture to always perform in a particular manner.

Yet, in this problem we also see where this process of learning and reconfigurability might prove to be more interesting because said scenario will allow designers and decision makers to judge and empirically see, Parameters what are the underrepresented factions of the user population. This knowledge can empower or better yet readjust itself for such underrepresented sectors of the population. In a certain sense this can be said then to be a tool for fostering social equity.

Evaluation

Rate

Reset Time

Critical Mass

Max Count

Distance

Prioritization

Spatial Result

Collective

Optimum Statisfaction

Individual Feedback

Parameter Fluctuation

Democratic Ideal

Collective Statisfaction

Optimization

Collective Statisfaction

Optimization

Inertia

Feedback

Transition Time

We can foresee two major limitations/challenges when operating under this framework. A lot of these resources and techniques like the neural network algorithms developed by machine learning experts are designed for problems which are believed to have a single answer or solution to them. Architecture is not a field that operates under those concerns for it can intrinsically accept a number of different solutions to a particular architectural problem. This “n” number of architectural solutions can all be said to solve the problems equally well and their performance is, at least in part, measured in a subjective manor. This difference will have to be ameliorated by coding in the ability for algorithms to explore multiple forms and configurations to problems previously seen before.

The project does not claim that it will provide the best solution to an architectural problem, simply because such a thing does not empirically exist. What this project strives to do is to distill a satisfactory solution from the multiplicity of configurations of a complex object.

N Optimum Urban Polemic Ideology World View Assumptions Evaluation

Designer

Prescription

Concensus/ Dissensus

Feedback

Optimizati

Optimizati

Disciplinary Relevance N Optimum Tyranny of the Majority Urban Polemic Ideology World View Assumptions Evaluation

Designer

Prescription

Concensus/ Dissensus

Feedback

Spatial Result

We see intelligence being developed in multiple ways outside of our profession, and even within the discipline people like Theo Jansen have been asking what is intelligence embodied in our physical realm, within the things that we build and how can we instill in them the particular values by which we see the world. But, the google car, your iphone’s natural speech recognition software, and deep dream are all attempts of intelligence being developed to perceive and understand the world around us and its environment. But we still have to see the significant ways that Strandbeast, Theo Jansen Deep Neural Nets manipulated images, Google Deep Dream project

architecture can add to this discussion and the large opportunity that lies when we flip the model around and allow the environment to model and understand us. When it is the environment that is studying and acquiring intelligence through your actions in physical space.

Methodology Part 3

The Generative Duration Drawing Program & Site Articulated Surface Mechanism Actuated Modules & Training Volume Solution States Network Representation Site Implementation Conclusion

The Generative Duration Drawing Operating under a paradigm of adaptive interaction, this design project was measured and held accountable by the way it organizes, deploys and operates actuated elements as they progress through time. Hence, the architectural drawing must be rethought and manipulated so as to align it with an agenda of representation that is able to present the reader with a multiplicity of spatial configurations under a single drawing. We present then the concept of the Generative Duration Drawing, a representational method that not only expresses the geometric nature of our objects but overlays all of its possible deployments in space.

We need to first answer some fundamental questions pertaining to change as a conceptual dimension and time as a phenomenological dimension to architecture. We need to go beyond the current fascination with mechatronics and explore what change means in architecture and how it is manifested… We need to explore the kinds of changes that buildings should undergo and the scale and speed at which they occur. We need to examine which changes are necessary useful, desirable, possible…” 5

Following the lineage of Jules Etinne Marey, we will key frame and index multiple states of actuation and overlay them to study their capabilities for movement. In a parallel effort we will diagram associations between radically different states and notate the respective human inputs that promote their configurations. The generative duration drawing is seen then as a tool for design that does not ignore the act of actuation but incorporates it in a way that unforeseen opportunities between mechanisms and human inputs can be reveled to both the designer and his audience.

Jules Etinne Marey

5

“Outlook” Alive - Branko Kolarevic

Student Generated Drawings

Program and Site

There was careful attention placed in the selection of the site and program under which these speculative ideas could be expanded as a conceptual design. As discussed, it is our concern to provide an array of discrete actuated architectural components that will gather data from inhabitants in order to infer a spatial solution that negotiates between conflicting parties.

Within the context of urban design one space that constantly tackles the negotiation between conflicting inhabitants is that of privately owned public spaces or POPs in New York. These are urban amenities, such as plazas, seating furniture, leisure areas and indoor cafeterias that are controlled by a private owner but open to the general public, often in exchange for a height variance on the host building.

Programmatically it is also important to develop a scenario in which multiple parties have different stakes on the limited space. Programs that can not only tolerate Feedback expansion, contraction and reconfiguration but might at times benefit from it. For the Actuation purposes of this project the program was defined as a privately owned public space that had the ability to negotiate between two main programs: Office spaces for software startups Multipluralilty of states and Negotiation Diagram

and an urban art gallery. Both of these programs at times expand aggressively as the need for development stages or the availability of artistic exhibition comes and goes, yet at times need to contract as development cycles and exhibitions end.

As such, the Lever House by SOM proves to be an urban scenario in which these questions were already attempted to be answered.

The lever house already holds a relationship between an art gallery and an office space, yet one that was designed with the modernist ideal that these two functions ought to be clearly separated and isolated from each other. What this project will then claim to do is to provide a multifunctional renovation to the lever house in which the pressures between the two conflicting programs can be negotiated in real time through the spatial configuration of the architecture.

Articulated Surface Mechanism

The initial phase of design research concerned itself with pinpointing various kinetic means by which to actuate a physical space. If our resultant architecture is to automate the reorganization of space then it is important to understand the multiple ways by which these changes in geometry can be mechanically achieved.

The articulating surface, being the first of these explorations, provided a way to achieve this change by deploying a flat array of linkages into a configuration that implied volume. The lengths of each linkage were parametrically modified so as to create the contour of any three-dimensional volume desired.

The resulting volume was then an artefact of the length parameter of these linkages.

These would actuate in unison, potentially controlled by one single actuator. This was explored parametrically through multiple digital A multiplicity of geometric volumes are implied as the articulated surface mechanism deploys.

simulations of the mechanism, and further researched through the creation of a physical model showing the most interesting of the iterations.

Because there was a drastic change in geometry produced as the articulated surface deployed, there was a need to index and represent its multiple states under one single drawing if one was to judge the kinetic qualities between iterations. This was then the basis for the first creation of a generative duration drawing for the mechanism.

Generative Duration Drawing

The articulated surface provides a kinetic system with enough variation to warrant a different form of representing its deployment.

A series of Generative Duration Drawings were developed to represent each iteration and its multiple states of deployment throughout time in a single drawing.

The mechanism is rendered in one of its fully deployed states, showing the potential for implied volume that emerges between linkages. Overlaid on top of it are abstracted key frames or indexes of its other positions as it actuates. By indexing a critical mass of these positions one can start to create graphical densities that codify those spaces where the mechanism is most likely to be throughout its deployment. The less dense areas are places where the mechanism passes by only momentarily. Under this we conclude

by diagraming the parametric inputs that determined the relative lengths of each linkage as well as codify via color in what direction the mechanism is to be actuated. Red being the fixed point in which the linkage pivots and blue being the end of the linkage that is allowed one degree of freedom on which to actuate.

The resultant drawing can be understood as recollection or under specified narrative of the kinetic nature of the mechanism.

Actuated Modules & Training

O

N

O

The articulated surface was a mechanism that explored the unison of many moving parts.

The actuated modules, being the second mechanism explored asked what was the potential of creating discrete parts that had a degree of autonomy in their actuation.

This mechanism also served as a platform on which to apply predictive algorithms as controllers of a physical artefact capable of changing its geometric configuration.

A central chassis was coupled with two arms that pivoted in different planes via a servo motor. Each of these arms was equipped with magnets that allowed it to mechanically snap with its neighboring unit. These robotic contraptions had then multiple ways of configuring themselves both in unison and O together as they found and linked with each other. Providing then a multiplicity of states to be controlled by the algorithm.

N

N

N

The modules could be trained to assume different behaviors and configurations based on previous training and human input.

A backwards propagation algorithm was allowed to then control both units in real time. For this, two inputs were created each a floating point number from 0 to 1. For each pairing of these two inputs the neural net’s output would be the angle for each servo that would prove to be most satisfactory. The degree of satisfaction was then measured through human input and formally fed as the

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training data for the algorithm. In order to do this, the training data set consisted of multiple instances containing: the current position of the two inputs, the current position of each servo and an evaluation number consisting of either a 0 (negatively reinforced) or a 1 (positively reinforced). The algorithm, as O more entries were added would retrain itself on the fly and would then, for its particular inputs, try to infer the servo angles that would predictively yield a positively reinforced rating in the future. Thus the modules could be trained via a series of binary yes’ and no’s.

This proof of concept was then tested as multiple participants would then train the modules to do particular behaviors for different inputs. For instance, one could train them to lay flat at input (0,0) and to stand up at (1,1).

O What is powerful about this method is that for an input of (1,0) or (0,1) the algorithm would infer what would be the best position, based on the previous ratings without having to be explicitly programed to do so. It could act as a mediator between conflicting forces, here abstracted to be a combination of 0s and 1s.

Actuated Modules & Training

The actuated modules provided, by their discrete nature and scale, a platform for the reorganization of space based on human input. The next step was then to digitally explore a scenario in which a critical mass of these modules would be deployed and trained to reconfigure themselves on the fly based on input.

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Diagrammatic drawings of the multiplicity of potential for the actuated modules.

Through parametric simulation, multiple configurations of the mechanisms were explored to reveal linear volumes or asymmetrical densities within the cloud of units. These configurations were then allowed to be altered, one module causing a chain reaction when changed because of the interconnected nature of the assemblage. The exercise revealed that although highly dynamic in low numbers there was a particular density of modules that when reached seemed to act in a volatile and incomprehensible fashion.

Volume Solution States

The generative duration drawing as a design tool had up to then been explored only in post rationalization. In order to apply it as a truly generative system a new abstraction had to be conceived. This came in the form of what we called a Solution Volume.

The sum of all the instances of a kinetic mechanism, as it deploys, implies a particular volume of space that could be inhabited at any one time. If one takes this analogy to its logical conclusion one can ultimately represent a mechanism and all of its complexity for movement with one single mesh or volume.

The surface of these Solution Volume can then be coded with the same information of positional probability via a heat map color coding. Our mechanisms were then abstracted to this volume that, in its representation of color, could communicate the probability and nature of its deployment.

Volume Solution States

Volume Solution States

Volume Solution States

Under this light, the morphology of various volume solution states were devised and then populated with kinematics that could approximate such actuation requirements. A series of expanding arms, coupled with fabrics were then introduced as a way of “solving� the implied movement of the designed volume.

This step proved to be important because under this methodology one can focus on the formal and performative qualities of the mechanisms first most. Shifting the conversation towards familiar terrains for architects and designers of space. The output is prioritized and it is the mechanism itself that is post rationalized. In a similar way, the performative aspects of the mechanism took on a distanced narrative from the actual kinematics of the actuated modules. As a space planing tool this proved to be powerful

as one could design the spaces and its partitions formally before having to worry about how to achieve them first.

The idea is that drawings like these can communicate significant information of actuated components and should form part of a regular set of construction drawings. It is also provides a computational benefit, for it affords us the ability to predict conflict and collisions between actuated elements before they are designed or placed on a site.

Volume Solution States

Actuated Fabric Mechanisms, Volumetric Solution is contained within the volume of its structure

Volume Solution States

Using the volumetric solution method, various mechanisms and assemblages were designed and tested. The use of fabric started to take a primary role in the “fitting� of these mechanical volumes for its ability to generate maximum surface with the minimum amount of structure.

Surface was also seen as a material that could interface with the human body, its soft qualities absorbing some of the uncertainty created when pairing up moving pieces of architecture and humans. Hence, the next generation of mechanisms consisted of assemblages between fabric and structure, where the tensile membranes where allowed to deform and reconfigure as actuators translated and rotated within the mechanism.

A particular interest arose for mechanisms that began to be self-contained within a their volume. If one could generate a modular array of self-contained spaces that within themselves had the ability to open, close or otherwise change their morphology, these could be deployed on the site and allowed to operate collectively as organizers of space.

From this line of thought, assemblages were thought of then as space making volumes within themselves that used tensile membranes as reconfigurable partitions.

The final mechanism chosen for the large speculation was derived from a Bitruncated Bitruncated Cubic Honeycomb determines inside which the mechanism transforms space

cubic honeycomb that was allowed to be deformed in order to provide walkable floor area.

The assemblage was then coupled with actuated elements from the mechanisms that were previously studied in order to generate fabric partitions that could be deployed or undeployed from a central column. The resultant structure is then informed by the honeycomb array but is always being internally reconfigured in order to open up or segment space as needed. The assemblage now constitutes of a modular configuration of volumes, kinetic tensile partitions and structure. Their discrete nature affords us, like Beesley, an array of elements that can function non-linearly at an architectural scale, in this case responding to the programmatic requirements of the inhabitants of such spaces.

Volume Solution States

Network Representation

This mechanical assemblage now has the potential to reconfigure itself into a wide variety of functions. Its geometry able to shift according to the intelligence allowed to operate it. This intelligence needs not to see itself in its entirety. It only has to concern itself with that which is actuated, the solution volume of the fabric partitions. Operating in accordance to the probabilistic mapping of its multiple positions. It only needs to see what floor area is being currently inhabited. Lidar scans and machine vision algorithms determine what is being occupied and by whom, and can then couple this spatial information with the feedback (both positive and negative) that the users are providing for the algorithm. It is possible then, to device a drawing that incorporates all of these views of the same space. Hard geometry, probability map and machine vision in one.

Through this representation it becomes possible then to hypothesize an architecture with the intelligence to interpret user-generated data. Fused within its spatial medium, this architectural

understanding of collective behavior can then be allowed to provide spatial repercussions, changes in geometry or program, in real time. Changes that reflect and are synchronous with our current and often changing metrics of value. For its intelligence will always be borrowed.

Always molded by the structures and patterns of our unsuspecting inhabitation of spaces and regurgitated through actuators, motors and other mechanical means. Thus this raises the question of how to design and represent such multi faceted spaces. What are the benefits of a pattern seeking, intelligent architecture that can geometrically transform? The answer could be found within the new affordances and trends that we’ve grown accustomed to in the information age.

A multi plurality of form is analogous to a multi plurality of information, entertainment, and goods offered to us through digital means. Hence we can speculate on the implementation of both predictive and suggestive procedures that help us process the uncertainty and provide a higher fitness.

Network Representation

Network Representation

What you see in these previous pages is the representation of the mechanistic view of such a space. The viewpoint from inside the algorithmic black box. An architecture that does not see surface or matter as planes but as data points with associated attributes. One that does not see movement as instance but as a probability map, where we can encode the propensity for matter to be present at a particular region in space.

Humans are not seen in their entirety, but rather revealed to the system through the channels with which it is allowed to interact with them. Feedback points consisting of positive or negative reinforcement are coupled with spatial coordinates that describe the collective notion of dissatisfaction or appraisal of the current spatial configurations. Yet, this view is still biased in some ways. It renders information within the context of perspective, all too familiar to us and distanced from the holistic nature of the algorithm. Thus there is a need to reduce the causal distance by projecting the same information into an unfamiliar

plane. Perhaps Stereographically or arranged polarly, this representation once projected now contains the same information as before but can no longer be consumed sequentially, instead it is rendered in unison as a holistic image.

More analogous to the processes it tries to represent and more attuned to the idea of the “collective� as a discrete and understandable entity within space.

Site Implementation 1

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The implementation of such a system into the large scale can then be described in a series of discrete steps following the organizational logic of the previously developed assemblage.

1. The Lever House is selected as site.

2. The column grid from the Lever House is abstracted and the lower levels are eliminated.

3. Following the Lever grid we introduce our Bitruncated cubic honeycomb as the new organizer of space.

4. Primary columns are allowed to extend onto the upper floors. These will serve as the anchor points for the kinetic mechanisms.

5. A secondary grid is introduce diagonally for circulation 6. Secondary structure is integrated between the two grids.

7. Vertical circulation and landings are introduced within the structure.

8. The column grid and secondary structure is populated with the actuated fabric partitions.

9. Floorplates are reintroduced in accordance to the new grid and fabric partitions.

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Site Implementation

What results then is a space akin to an open floorplan yet one that behaves entirely different. As partitions segment and renegotiate that which is office and that which is gallery, patterns, openings and clearings form, distort and disappear as feedback is introduced. Predictive patterns emerge that foresee trends in usage before they are necessary. Configurations that work are kept, but constantly reevaluated, change is always in question. By pairing up with the existing grid there is a coherent transition between gallery and office space.

One important concern here is scale. How big do you allow a single unit of space to be? The discrete unit of space has to be big enough so that if fully segmented, it is usable for either program. Yet, small enough so that the pairing and expansion with its neighbors

is both beneficial and desirable. Vertical circulation can then be seen as the disruptive element in the grid. As it transverses through the different levels it is allowed to expand and reach out to all segments at multiple locations because it is subject to be used in many different ways throughout the life of the building. Existing elements within the Lever House such as vertical circulation are kept, but are simultaneously renegotiated through the deploying partitions.

Still, the tensile membrane partitions are the protagonist of space. As both agents of separation and unison the vaulted nature expressed in all of its deployment states strives to bring a coherency to the interior as a whole.

Site Implementation

Conclusion

The representation of such a volatile space is one that by its very nature is hard to represent.

Rendered in any one of its current states one misses the point, the richness of the back-door processes that are at work behind the curtains. As people learn to transverse and coexist with this organizing scheme a new affordance or nature of inhabitance is likely to occur. Our urban context as we tend to understand it is imposingly static. It is distanced and alienated from our actions, we on the contrary are expected to mold to it. Hence an architecture that by its very nature molds to us, falls under foreign and unexplored territory within our disciplinary domain. Only through experimentation will we see what qualities work and how does the body react to change at this scale. This architecture introduces yet a new element in our already saturated soup of urban

experiences, yet one that has the power to provide a desirable and measurable benefit.

It allows us to form part of the conversation between us and the urban environment. To be agents of change when we gather in numbers and to regain the ability to disrupt old spatial hierarchies. Replacing them with new ones, hopefully a little bit more equitable, hopefully a little bit more reactive to our changing values. In a world that moves increasingly faster it is hard to comprehend what paths are worth pursuing or best for everyone. Hence an architecture that allows us to fail, that absorbs the uncertainty of creating mistakes, giving people the ability to renegotiate and reconfigure what is rightfully theirs, the right to not be imposed upon, is in my worldview an innately human architecture. Regardless how old, new, exact or poorly understood its means are to achieve this purpose are.

Bibliography Fox, Michael, and Miles Kemp. Interactive Architecture. New York: Princeton Architectural, 2009. Print.

Jaskiewicz, Tomasz . “Approaching Distributed Architectural Ecosystems” Alive: Advancements in Adaptive Architecture. N.p.: n.p., n.d. N. pag. Print.

Kolarevic, Branko . “Outlook.” Alive: Advancements in Adaptive Architecture. N.p.: n.p., n.d. N. pag. Print.

Sterling, Bruce. Shaping Things. Cambridge, MA: MIT, 2005. Print.

Landa, Manuel De. Philosophy and Simulation: The Emergence of Synthetic Reason. London: Continuum, 2011. Print.

Celik Alexander, Zeynep. “Neo-Naturalism.” Log. N.p.: Anyone Corporation, 2014. N. pag. Print.

Menges, Achim, and Sean Ahlquist. “Computational Design Thinking”. Chichester, UK: John Wiley & Sons, 2011. Print.

Chu, Karl. “Metaphysics of Genetic Architecture and Computation”. N.p.: n.p., n.d. Print.

Negroponte, Nicholas. Soft Architecture Machines. Cambridge, MA: MIT, 1975. Print Kitchin, Rob, and Martin Dodge. Code/space: Software and Everyday Life. Cambridge, MA: MIT, 2011. Print.

Pickering, Andrew. The Cybernetic Brain: Sketches of Another Future. Chicago: U of Chicago, 2010. Print.

Ballard, J. G. Vermilion Sands. New York, NY: Carroll & Graf, 1988. Print.

Frazer, John. An Evolutionary Architecture:. London: Architectural Association, 1995. Print.

Beesley, Philip, and Pernilla Ohrstedt. Hylozoic Ground: Liminal Responsive Architecture. Cambridge, Ont.: Riverside Architectural Pr., 2010. Print.

Gerber, David Jason, and Mariana Ibanez. Paradigms in Computing: Making, Machines, and Models for Design Agency in Architecture. N.p.: n.p., n.d. Print.

Credit I would also like to give credit to the following instructors who’s work and course material were instrumental to the thesis.

Mohamad Al Khayer PhD. - Deployable Structures - Experiments in Structures Shawn Rickenbacker - Non Discrete Architectures: Digital Prosthetics, Connectivity and Augmented Space ­

PennDesign 2016