artificial intelligence in structure

Artificial Intelligence in Structure

Master’s ThesisAlan MacejewskiDrury University

2014-2015

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Table of ContentsAbstract

Case Studies

Research Summary

Google Nest Thermostat

Responsive Material System

Tensegrity Structural System

Site Selection

Program Selection

BibliographyAppendices

Background Research

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Fig. 1.1 - Tensegrity Structure

Abstract


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Abstract Background Research Case Studies Research Summary

Abstract Given that buildings account for the majority of energy consumption, building efficiency should always be a top priority. Unfortunately, since current buildings systems do not have the ability to operate beyond their maximum efficiency specifications, their maximum efficiency level is predefined by their design. Applying artificial intelligence to building systems will allow the building systems to respond to changing conditions creating continuous improvements to efficiency. Understanding artificial intelligence and its capabilities is imperative when applying functions of artificial intelligence to building systems. The basis of artificial intelligence stems from the most general form of intelligence: learning and adapting. Adaptation in architecture is achieved through a process that evolves over time. Once adaptation is achieved, aspects of architecture will fully possess the learning qualities needed to respond to conditions and improve efficiency. The strategic application of artificial intelligence in buildings will begin with the building systems. HVAC, electrical, plumbing, and structural systems currently determine how efficient a building can be. Applying artificial intelligence to these systems will have the greatest immediate impact on building efficiency. Beyond current building systems, the ever-growing Internet of Things integrated with adaptive building materials and tensegrity structures creates the potential for adaptive structures in architecture. Once adaptation is achieved, can building structures begin to shape and reshape building forms in response to conditions to improve efficiency?

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Program Selection Site Selection Bibliography Appendices

“Intelligence is the ability to adapt to change.” -Stephen Hawking

“Adapt or perish, now as ever, is nature’s inexorable imperative.” -H.G. Wells

“Times change and you have to adapt.” -Jerry Cantrell

“The more you adapt, the more interesting you are.” -Martha Stewart

“You are cruising along, and then technology changes. You have to adapt..” -Marc Andreessen

“Intelligence is based on how efficient a species became at doing the things they need to survive.” -Charles Darwin

“One general law, leading to the advancement of all organic beings, namely, multiply, vary, let the strongest live and the weakest die” -Charles Darwin

Background Research


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Background Research Humans, as intelligent beings, possess the abilities to learn and apply knowledge and skills, to adapt by changing behavior based on experiences and to reason. The simplest behaviors of humans often require some intelligence. In contrast, seemingly complex actions taken by animals may lack intelligence entirely. When the female Digger Wasp returns to her burrow with food, she first leaves the food outside and checks inside the burrow for intruders before entering with the food. If the food is moved just a short distance while the wasp is in the burrow, she will notice the change in location when she returns outside and once again check inside for intruders. This unintelligent response can be repeated indefinitely proving that the wasp lacks the ability to learn and adapt. Prior to the birth of Fig. 3.1 - Digger Wasp

Site SelectionProgram Selection

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Bibliography Appendices

artificial intelligence, machines acted in a similar unintelligent manner. Artificial intelligence can most accurately be defined

as the science of making man-made computational machines act in an intelligent manner by reasoning and learning. Perhaps the most basic form of machine-learning is that of trial and error. Learning through trial and error is simply receiving undesired or incorrect results until the desired or correct result is achieved. In this case, the ability to adapt and become intelligent relies on being able to recall the desired result if the same situation presents itself. A simple computer program using trial and error to make the best chess moves to win a game would run through all possible moves and decide accordingly. After achieving success, the program would remember the moves used for victory in that situation and apply them again if the same situation occurs. Trial and error machine-learning falls into rote

Fig. 3.2 - AI vs. Human

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learning, which is often viewed as rudimentary given that there is a lack of real understanding and reasoning behind the process of learning. Reasoning involves making an inference that is drawn from evidence in a certain situation. These inferences can either be deductive or inductive. Deductive reasoning involves using generalizations to form a particular conclusion, whereas inductive reasoning involves using particular examples to create generalizations. As an example for deductive reasoning, the general statements “All people from Texas are cowboys. You are from Texas.” result in the particular conclusion “Therefore, you are a cowboy”. For inductive reasoning, the particular statements “You are from Texas. You are a cowboy result in the general conclusion “Therefore, everyone from Texas is a cowboy”. When applied to machines and artificial intelligence, computer programs must have success in order to draw inferences, especially deductive inferences. Reasoning involves drawing inferences that are both relevant and desired to a situation. However, computers cannot simply reason to create inferences. This in turn gives way to one of the most difficult issues facing artificial intelligence - the ability to distinguish

Fig. 3.3 - Jibo robot

Fig. 3.4 - Jibo robot


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between relevant and irrelevant information. Problem-solving in computer programs and artificial intelligence often include a basic form: “given this, find that”. Artificial intelligence is used to resolve a wide variety of problems including picking out someone in a picture, moving objects from one point to another, and determining the moves required to win a game of chess. There are two types of problem-solving artificial intelligence can utilize to come to a conclusion: special-purpose and general-purpose. Special-purpose problem-solving takes explicit features in a situation and applies them to a process that is made specifically for that purpose. On the other hand, general-purpose problem-solving can be applied to a variety of situations. An example of this in artificial intelligence would be a “means-end” analysis where the program systematically compares its current state and takes steps toward reaching the desired goal. These steps may be movements toward a location or perhaps lifting an object to a specific place. Early examples of artificial intelligence and

Fig. 3.5 - Robot lifting

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intelligent machines can be found in Greek Mythology and 19th century literature; however, there has been an eruption of intelligent machines since the 1940s. Artificial intelligence has advanced in such a way that its uses in modern society stretch across numerous industries. One of the first uses of modern artificial intelligence was seen in computer programs for computer science, allowing more difficult problems to be solved for all types of programming. The financial industry uses artificial intelligence to manage the markets and trades. Numerous industries use artificial intelligence to place robots in situations that may be too dangerous for humans. For architecture, modern artificial intelligence places itself within homes and more specifically throughout objects in homes. “Smart homes” combine the use and capabilities of the Internet with inanimate household objects. Many are familiar with lighting or entertainment systems that use artificial intelligence. Recently, artificial intelligence has been incorporated into doors, refrigerators, thermostats, and other appliances enabling these appliances to respond to human motions and indoor conditions. Some of the most popular uses of artificial intelligence in homes are for efficiency purposes. A modern example of this is the

Fig. 3.6 - Nest Smartphone app

Fig. 3.7 - Smart home


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Nest thermostat owned by Google. The Nest thermostat uses artificial intelligence to learn and adapt to the users’ preferences. After just a few days of being adjusted to ideal temperatures, the thermostat will build a personalized schedule to improve efficiency and comfort. Adaptive architecture can be said to be responsive architecture evolving over time. Perhaps the biggest question is, when is adaptation achieved? Throughout time, a series of points and functions must be reached to ultimately achieve adaptation. These points include responsiveness, dynamics, kinetics, and finally adaptation. In architecture today,

Fig. 3.8 - Smart home

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we can find structures that begin to include properties of responsive architecture; however, the ultimate goal is adaptive architecture. The process of reaching adaptive architecture is an evolutionary process that could take generations or just a few months. For example, a traditional building façade or skin that “provides stability, regulates air pressure (fenestration) and protects the interiors from direct environmental factors (sunlight, rain and wind)” (Verma) would not be

considered a responsive or adaptive building element. For a building skin to be completely adaptive, it must have responsive, dynamic, and kinetic properties. Responsiveness involves an element changing its behavior based on its surroundings at the most basic level. An example of responsiveness in architecture would be electrochromic glass which can change from

Fig. 3.9 - Adaptation process


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translucent to opaque and back with an electrical charge. This and other examples of responsive architecture still lack dynamic and kinetic qualities which will allow responsive architecture to evolve into adaptive architecture over time. Dynamics can be loosely defined as something that continuously changes, grows, or develops. If a dynamic quality were applied to electrochromic glass, then every shade between fully translucent to fully opaque could be attainable. Having this dynamic quality would allow the glass to function in an increased number of situations more successfully. The final quality required for the evolution from responsive to adaptive architecture is kinetics which involves objects relating to, causing, or producing movement. Giving electrochromic glass the ability to move

Fig. 3.10 - Adaptation process

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or adjust its position would again allow the glass to be increasingly more successful in its functions. Upon having responsive, dynamic, and kinetic qualities, architecture can begin to account for more situations and even open new possibilities for design. Buildings with adaptive qualities would have fewer limiting variables. Adaptive architecture can change its behavior or function based on what it has learned using the responsive, dynamic, and kinetic qualities to adjust to new conditions. Given the properties required to achieve adaptive architecture, buildings could then begin to account and adjust to any situation that may present itself.

Fig. 3.11 - Kinetic adaptation


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Fig. 3.12 - Modeled form

Case Studies

Fig. 4.0 - Tensegrity model

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Google Nest Thermostat

As demands for higher efficiency in building systems increase, technology will have to be applied to see a significant improvement. The use of technology and the Internet will be the standard for creating more efficient building systems. A large piece of the ever-growing puzzle of technology in building systems is the Nest thermostat by Google. With its ability to learn the living conditions that the owner prefers, the Nest “smart” thermostat is unique in the marketplace. The thermostat is able to learn and remember the user’s preferences even as conditions change. This enables the Nest thermostat to become more efficient, to create a more comfortable environment for the user, and to save the user money over time. There are even functions within the Nest thermostat that allow it to be programmed and altered remotely through smart phones. Fig. 4.1 - Google Nest


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There has always been a standard to building systems, specifically those that consumers have control over. The standard has changed in a way that demands higher efficiency with an increasing amount of automation. For many years, household thermostats simply allowed users to adjust the conditions in their homes by setting temperature thresholds. With the introduction of the programmable thermostat, these temperature thresholds could be modified automatically depending on the time of the day and the day of the week thereby increasing efficiency and comfort. Using adaptation, the Nest thermostat takes efficiency and automation of comfort to a totally unique level. The learning capability of the Nest thermostat actually allows the thermostat to continuously adapt to the Fig. 4.2 - Google Nest app

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user’s preferences and improve its efficiency over time. The 2nd generation of the Nest thermostat extends its adaptive capabilities to control all “smart” objects in a home. It is essentially a device that controls the “Internet of Things”. The Internet of Things is a “scenario in which objects, animals or people are provided with unique identifiers and the ability to transfer data over a network without

requiring human-to-human or human-to-computer interaction.” (Rouse 1) Wireless devices account for the majority of the Internet of Things given their increased capabilities. Examples the Internet of Things include “a person with a heart monitor implant, a farm animal with a biochip transponder, and an automobile that has built-in sensors to alert the driver when tire pressure is low.” (Rouse 2) More specifically, anything that can be assigned an Internet Protocol (IP) address and connected to the Internet is considered a “thing” within the Internet of Things.Fig. 4.3 - Smart object nest


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Being constantly connected to the Internet, controls a “nest of” smart objects. In time, the Internet of Things will include additional objects in homes that the Nest thermostat can control. There is already a Nest security system that works in tandem with the Nest thermostat’s adaptive capabilities. Technology is moving towards responsive automation in that the smart objects in homes will react to the user’s interest through the Nest thermostat system. An example of this is the interaction between the Nest thermostat and Jawbone, an armband that tracks the fitness of the person wearing it. When the person wearing the armband wakes up, the Jawbone system alerts the Nest thermostat. The Nest thermostat then controls

Fig. 4.4 - Google Nest features`

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desired objects in the home to create a more comfortable, responsive environment. This may include warming or cooling the house based on previous settings, starting the coffee pot, starting a cycle of a clothes dryer, etc. Google continues to develop the Nest thermostat system to control and automate additional processes within the home to makes lives easier and improve efficiency. While the adaptive capabilities of the Nest thermostat can positively impact our lives, there is an ethical side to this technology that must be addressed to ensure it can continue to be used for its initial intentions. It was recently reported that Google’s Nest thermostat was hacked. Although this singular incident may not be that worrisome, this may prove to be a scary reality for what is to come. The device was hacked by installing a custom version of the Nest thermostat firmware that gave that particular user root access to the device. From a remote location, that individual gained full Fig. 4.5 - Nest security system


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control over the Nest thermostat, all smart objects controlled by the Nest thermostat, and the entire home network used to operate the Nest thermostat. Full control over a home network by a hacker means that all data files, financial information, personal information, and more are now at the hacker’s fingertips making the homeowner vulnerable to identity theft and privacy violations. Google is even concerned about the possibility that Nest thermostats may be bought wholesale, hacked by an individual, and then resold to the public. This means that as soon as these Nest thermostats are setup in homes, everything involved with the home network is potentially compromised. These security issues will have to be addressed for the continued success of the Nest thermostat. Fig. 4.6 - Hacked Google Nest

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In recent months, students at the Institute for Advanced Architecture of Catalonia (IAAC) have been able to produce a responsive material. The goal of the project was to produce a material that could be manipulated in a way that responds to user desires. The material is able to be reformed based on a set of parameters that are predetermined by the user or potentially controlled remotely by users for a desired result. The students successfully created a material that is completely reformable given its basic structural limitations. The push for adaptation or responsiveness in architecture is an ongoing effort, so creating building elements that could be programmed or manipulated to respond to situations is a positive step in that direction. The possibilities that this responsive building material has opened up are far reaching given its capabilities and applications. The responsive building material essentially is a “series of transformations between forces, material phases, people, spaces, and functions.” (Shami) There is no way to predict a


Fig. 5.0 - Material system exploded


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given situation, so changing form in response to unpredictable situations would be ideal. The idea that form follows function is taken to a new level in that altering forms means altering functions. The building material created utilizes Shape Memory Polymers (SMP) which can take a new shape when introduced to different situations. For this development, the SMPs are setup as a hexagonal node to hold a triangulated tessellation. The transformation occurs when the material is heated above 60 to 70 degrees Celsius for three minutes. At this point, the material takes on a malleable, rubbery state with a low tensile strength which can undergo geometric deformations. The actual transformation of the material is achieved through octocopter drones pulling and adjusting the structure based on previous programming or user input. After

Fig. 5.1 - Material system diagram Fig. 5.2 - Material system model

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Fig. 5.3 - Material system tessellation


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two minutes, the structure is set in place until it is reheated. What results is a structure that can be continuously reformed into new structures based on situations in the environment. The key properties of the responsive material system include how it is formed, what this form allows, and how this material could eventually impact design. The material gains its form through a triangulated tessellation that responds to SMPs change in state from heat. While this process is basic in that it is simply a phase change, it is the artificial intelligence of the drones that continuously gives the material a unique form. The drones can be programmed in a way to respond to things such as climate, spatial requirements, or human interaction. Artificial intelligence within the drones creates the responsive behavior of the drones. This intelligence is then translated to the materials for the desired result. The artificial intelligence of the drones allows the form to react to nearly

Fig. 5.5 - Material system form

Fig. 5.4 - SMP process

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any situation required. Perhaps the greatest advancement for artificial intelligence in architectural building materials will be seen when this responsive material can be applied to buildings. The students created a building material that is completely responsive and reformable to suit changing situations. Now that the technology and artificial intelligence of the responsive material exists, it is just a matter of removing its current limitations. The responsive material is limited in a few ways: its triangulated tessellation, SMP

Fig. 5.6 - Material system form

Fig. 5.7 - SMP test

Fig. 5.8 - SMP configuration


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hexagonal shape, and how the shape is achieved. The form that the material can ultimately take is extensive, however, because there are hard edges within the structure, there is only a certain number of shapes the form can take. The limit of forms could be altered or increased by reducing the edges or changing the shapes of the SMP or tessellation. To create and form the shape, there currently must be programmed drones to push and pull the material to achieve the result. Ideally, the capabilities and artificial intelligence of the drones would occur within the material, allowing it to work and respond independently. The material is said to already be solar powered and remotely controllable, so the framework for a fully functioning responsive building material is set in place. When the limits of the responsive building material are reduced or completely removed, there begins to be an increasing number of possibilities for this material within architecture. Given that the responsive building material could function independently as a series of artificial intelligent nodes to control its structure, this building material could be applied to buildings as the primary material, envelope, or even structure. Building facades would quickly see this responsive material applied to adapt to all

Fig. 5.9 - Material system transformation Fig. 5.10 - Material system transformation Fig. 5.11 - Material system transformation

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situations in climate and program. The responsive material could even be seen as a building envelope, carrying the function of creating and defining space. Taking this material further, it could become a building’s structure. Given that adaptation can occur to a material, it could also be applied to structure to allow for a responsive capability. Having a building structure that is responsive would allow the entire building to be presented in a different way or adjusted in a way as to account for earthquakes, impacts, or other events. The possibilities of a responsive building material can again be translated into numerous applications that would have a great impact on architecture and design for the future. Fig. 5.12 - Octocopter transformation

Fig. 5.13 - Material system Fig. 5.14 - Material system


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Fig. 5.15 - Material system & octocopter

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Fig. 5.16 - Transformation Fig. 5.17 - Transformation

Fig. 5.18 - Transformation



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Fig. 5.24 - Transformation Fig. 5.25 - Transformation


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Tristan d’Estrée Sterk has become known for developing strategies that will improve efficiency in buildings by applying a responsive element to them. By changing building shape and color, efficiency has been proven to increase by eight to thirty percent. Larger percentages of efficiency improvements are reached when building shapes become responsive to their environment. Given this, Sterk has applied a responsiveness capability to the ideas of Tensegrity by Kenneth Snelson and Buckminster Fuller. Sterk’s goal in his Filamentosa project was to directly apply responsiveness in changing shapes to forms in tensegrity structures to improve efficiency. Perhaps what makes

Tensegrity Structural SystemFig. 6.0 - Tensegrity structure


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tensegrity structures different and essentially better than other structures lies in its properties. Traditionally, structures are built through a “brick-on-brick” idea of construction, where the members are stacked purely in compression. What Fuller imagined was a world that moved away from conventional structures and building standards and included rather unconventional structures that maintain their stability through tensional forces instead of compression. Fuller’s student, Snelson, first developed a sculpture that was defined as a tensegrity structure. Included in the sculpture were

Fig. 6.1 - Tensegrity structure


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two elements that existed together, compressive resistant struts and tensioned cables. Producing a tensegrity structure from these elements involves the compression members only contacting the tensioned cables. Both Fuller and Snelson define this interaction as “continuous tension, discontinuous compression”. Tensegrity structures are also quite stable. This stability is created by the minimal use of

rigid members and the fact that both compressive and tensile components are in equilibrium. This equilibrium within the structure is achieved by the interaction of the compressive and tensile elements: “cables pull on both ends of the struts, while the struts push out and stretch the cables.” (Ingber and Landau) The resulting structure is made up of elements that are already stressed, which means the compressive elements are already compressed and tensile elements are already tensed. This idea of stressing each other becomes known as “self-stress” or “pre stress”. While these basic properties of tensegrity structures do hold



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value, it is within the resilience of the structure that Sterk begins to use responsiveness to improve efficiency. Tensegrity structure’s resilient nature allows its components to be deformed and reoriented while not breaking, creating the possibility of structures that can change shape. Taking this a step further, tensegrity structures are also modular allowing a structure to “combine with another to form a larger tensegrity system.” (Ingber and Landau 2) Within larger systems, individual instances of tensegrity can even be disrupted without compromising the whole. Sterk’s project, Filamentosa, strives to` use tensegrity structures to demonstrate how responsive architecture can improve building efficiency by more than 30%. Responsiveness is first applied to a tensegrity structure which could include the structure of a building. Because of its resilient nature, the built structure is allowed to move. The ability to move means that responses can be setup to create intended

movements. The scale of Sterk’s model of tensegrity structure is small, yet given its modular properties, this idea can be translated almost exponentially. With the combination of tensegrity structures and responsiveness through changing forms, Sterk’s project can serve as a framework for a building structure that could inherently move and respond to improve efficiency in the years to come.

Fig. 6.4 - Sterk’s Filamentosa

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Fig. 6.5 - Sterk’s Filamentosa Fig. 6.6 - Sterk’s Filamentosa Fig. 6.7 - Sterk’s Filamentosa


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Research Summary


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Google Nest

• Improvingbuildingefficiencyisahighpriority• “Smart” objects improve efficiency through artificial intelligence• Theautomationandadaptationinsmart objects improves efficiency• Remotecontroloverenvironmentsimproves efficiency potential• Result is an “Internet of Things” that automate certain functions• CurrentlymostlydependentonInternet connectivity• Ethicsinnewtechnologydeterminespossible positive or negative impacts• Framework for Internet of Things• Adaptation is achieved

Research Summary

Fig. 7.1 - Google Nest features


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• Responsivenessiswhatallowselementsto be most efficient• Responsiveness becomes important as unpredictable situations increase• Artificial intelligence allows abundant exponential responses• Therearematerialsthatholdqualitiesto respond to situations• Change in behavior or function is the responsiveness in artificial intelligence• Artificialintelligencereducesthelimitsto responsiveness• Responsive qualities can be translated to building elements or building systems

Fig. 7.2 - Material system process

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Tensegrity Structural System

• Potential for most efficiency lies within a building’s ability to change shape• Fusingtheartificialintelligenceofchanging forms to the responsiveness of situations provides the most potential• Tensegritystructureshavequalitiesthatallow for changing form• Would implementing artificial intelligence with tensegrity provide the greatest improvement of efficiency?• Wouldthesequalitiesdisplayadaptationina successful manner?• Could tensegrity be used to form the structure of a building?• Could tensegrity as structure utilize artificial intelligence to achieve adaptation?



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Fig. 7.4 - Parametric model

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Research acquired from the basics of responsiveness, adaptation, the Google Nest thermostat, a responsive material system, and tensegrity structures all point to a few common themes: change in behavior, efficiency, and building structure. The goal would be to determine a way in which these subjects can coexist. Once architectural designs reach adaptation, these designs can begin to address and respond to an abundance of conditions. Given the numerous possibilities, it became important to diagram the possible conditions a building could respond to. The primary conditions a building could respond to are found within environmental, sustainability, and structural categories. These categories include subcategories which cover specific situations or events. Choosing just one of the subcategories would not present enough of a purpose for design, so the challenge lies in discovering how the subcategories are similar and can fit together to form a cohesive, adaptive design. Ruling out the most extreme conditions was the first method of narrowing the focus. Next, the conditions were compared to one another to determine which had the most in common. Finally, taking some of the most common conditions from each major category helped develop a basis for what kind of design would allow for adaptation. Responses to the basics of the environment, key aspects of sustainability and the fundamentals of structure could all begin to come together to function in one building. After determining which conditions a building could respond to, the key findings from the case studies (Google Nest thermostat, responsive material system, and tensegrity structural system) helped narrow the focus for a formal program. In fact, the ideas highlighted in the research summary build upon each other to form a direction in developing a program. The Google Nest thermostat currently represents the most basic form of adaptation and will serve as the framework for a larger set of objects with adaptable qualities. The responsive material system takes responsive and adaptable qualities and applies


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Fig. 8.0 - Conditions webb

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Fig. 8.1 - Conditions webb


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them to a faceted structure. This structure with kinetic capabilities could serve as a building façade or structure. The tensegrity structural system, with its resilience, has the ability to reshape or morph itself while still preserving its strength. With these ideas taken from the case studies, it is possible to begin to see how they can build upon each other to form a single idea - a structural system with adaptive qualities that can reshape itself to increase efficiency.

Fig. 8.2 - Google Nest Fig. 8.3 - Material system Fig. 8.4 - Tensegrity structure

Fig. 9.0 - Built model

Program Selection

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Program Selection Using the idea of an adaptive structural system that improves efficiency and the possible conditions a building could begin to respond to provides for an apparent direction to develop a program. With conditions such as climate control, a building could account for changes in the environment while improving efficiency. Then, is it possible to incorporate structure into this idea? The structure itself could be used to enable the building to become more efficient. This could be done by taking advantage of adaptation and its properties. Given that adaptation would allow for numerous responses to different situations, applying adaptation in the form of artificial intelligence to a structural system would give the building the opportunity to change its shape in response to climate. Putting all of this together would result in a building with a structure that can reshape itself in response to a specific climate change, thereby increasing the building’s efficiency.

Fig. 9.1 - Water collection

Fig. 9.2 - Chilled water loop


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To be more specific, the building would respond to these environmental conditions: wind, rain, temperature, and sunlight. In response to these conditions, the building would improve quality airflow, collect water, reduce solar heat gain, and capture solar energy. A tensegrity structural system with adaptive qualities would then be implemented to serve as the literal structure for the efficient responses to specific climate conditions. Defining a program for a building in a hot, arid climate would best utilize the identified adaptation qualities and environmental conditions. This adaptive building would have the capability to reshape its structure in response to climate change. In a hotter climate, the structure could reform to allow for natural airflow. If a chilled water loop system were applied along the structure, the natural ventilation could be conditioned and cooled before entering space within the building. The building’s structure, coated with solar panels, could also reform itself to gain the greatest amount of energy throughout the day. In a hot climate where water is a precious resource, the structure could reform itself to efficiently collect the most rainwater. These adaptive capabilities existing in unison would make for an efficient, reformable building.

Fig. 9.3 - Chilled water loop Fig. 9.4 - Chilled water loop


Site Selection

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Site Selection

Given the building program has been determined to respond to climate conditions through a reforming structure integrated with a closed chilled water loop system, a hot, arid climate will serve as the site. The region chosen because of its hot, arid climate is Phoenix, AR. Phoenix is considered to be a desert climate, meaning there is not enough precipitation per year to sustain ample vegetation. Choosing an urban site was important given the desire for a large population using the building. Phoenix, as the hottest city in the United States with the highest average temperature, became the clear choice. While averaging over one hundred days per year with temperatures over ninety-nine degrees Fahrenheit, Phoenix is the ideal location for a building responding to a hot and arid climate.

Fig. 10.1 - Hot days

Fig. 10.2 - Average temperature


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Fig. 10.3 - Map of Phoenix

Fig. 10.4 - Phoenix temperature

Fig. 10.5 - Phoenix climate

Fig. 10.6 - Downtown Phoenix


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Appendices

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AppendicesFigure 1.0 - http://tharit.wordpress.com/2010/01/23/tensegrity-structure/#jp-carousel-202Figure 1.1 - http://cerebrovortex.files.wordpress.com/2012/09/snelson-weave-terahedron-tensegrity-2mage-161.jpgFigure 2.0 - http://cerebrovortex.files.wordpress.com/2013/08/tensegrity-floating-tension-web.jpgFigure 3.0 - http://pr2014.aaschool.ac.uk/submission/uploaded_files/DIP-03/andrew.bardzik-PA04.jpgFigure 3.1 - https://farm5.staticflickr.com/4125/4978550980_3eff02a4e2.jpgFigure 3.2 - http://www.newscientist.com/blogs/onepercent/2011/12/20/AB21225.jpgFigure 3.3 - http://i2.wp.com/techmash.co.uk/wp-content/uploads/2014/09/jibo.jpg?resize=1000%2C452Figure 3.4 - http://www.jebiga.com/wp-content/uploads/2014/07/JIBO-7.jpgFigure 3.5 - http://www.maker-party.com/uploads/2/5/8/9/25896237/5764602.jpg?687Figure 3.6 - http://www.buildingonline.com/eupdate/images/nest-labs-app-thermostat-300x260.jpgFigure 3.7 - http://www.republicmortgage.com/wp-content/uploads/How-To-Make-Your-Home-A-Smart-Home.jpgFigure 3.8 - http://smarthomeenergy.co.uk/sites/smarthomeenergy.co.uk/files/images/smart-home_0.jpgFigure 3.9 - http://www.adaptiveskins.com/Figure 3.10 - http://www.adaptiveskins.com/Figure 3.11 - https://fbexternal-a.akamaihd.net/Figure 3.12 - http://3.bp.blogspot.com/-RDQvnE137s4/VECB6c4gAXI/AAAAAAAABSs/T7NIlYzMQqo/s1600/Duality.jpgFigure 4.0 - http://www.archiprix.org/projects/2009/P09-1398/P09-1398_7227_blowup.jpgFigure 4.1 - http://www.fastcodesign.com/3032325/the-nest-thermostat-is-now-much-more-than-just-a-thermostatFigure 4.2 - https://nest.com/blog/downloads/2012-04-05/Energy-History.pdfFigure 4.3 - http://www.opinno.com/sites/Figure 4.4 - https://nest.com/thermostat/saving-energy/#your-energy-partnerFigure 4.5 - http://architect.hw.curationdesk.com/files/2014/01/Nest_Smoke_HERO.png


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Figure 4.6 - http://venturebeat.files.wordpress.com/2014/08/google-nest-hacked.jpg?w=655Figure 5.0 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-05.jpgFigure 5.1 - http://www.designboom.com/architecture/iaac-translated-geometries-08-19-2014/Figure 5.2 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-11.jpgFigure 5.3 - http://www.archdaily.com/546834/iaac-students-develop-material-systemFigure 5.4 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-02.jpgFigure 5.5 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-01.jpgFigure 5.6 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-09.jpgFigure 5.7 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-03.jpgFigure 5.8 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-03.jpgFigure 5.9 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-07.jpgFigure 5.10 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-07.jpgFigure 5.11 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-07.jpgFigure 5.12 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-12.jpgFigure 5.13 - http://vimeo.com/100694919Figure 5.14 - http://vimeo.com/100694919Figure 5.15 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-08.jpgFigure 5.16 - http://vimeo.com/100694919Figure 5.17 - http://vimeo.com/100694919Figure 5.18 - http://vimeo.com/100694919Figure 5.19 - http://vimeo.com/100694919Figure 5.20 - http://vimeo.com/100694919Figure 5.21 - http://vimeo.com/100694919Figure 5.22 - http://vimeo.com/100694919Figure 5.23 - http://vimeo.com/100694919

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Figure 5.24 - http://vimeo.com/100694919Figure 6.0 - http://www.aoe.vt.edu/images/people/webpages/csultan/M_arch.jpgFigure 6.1 - http://newsoftomorrow.org/wp-content/uploads/2013/02/Image51-700x528.pngFigure 6.2 - http://tensegritychiro.com/our-approach/what-is-tensegrity/Figure 6.3 - https://sage2014.wikispaces.com/TensegrityFigure 6.4 - http://tensegrity.wikispaces.com/file/view/filamentosaFigure 6.5 - http://newsoftomorrow.org/arts/archi/tristan-destree-sterk-structures-tensegrites-les-nouvelles-architecturesFigure 6.6 - http://newsoftomorrow.org/arts/archi/tristan-destree-sterk-structures-tensegrites-les-nouvelles-architecturesFigure 6.7 - http://newsoftomorrow.org/arts/archi/tristan-destree-sterk-structures-tensegrites-les-nouvelles-architecturesFigure 6.8 - http://bobwb.tripod.com/snelson/cubemetamorphosis.htmlFigure 7.0 - http://api.ning.com/ZQQ23Z*c7xKvlvwpzQeJSZtDmcuThT*wiXe3fzcshPSQLmOIhpi*1Nmy1LI5w8g_/1.jpgFigure 7.1 - http://support-assets.nest.com/images/000001081/reading-nest.pngFigure 7.2 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-10.jpgFigure 7.3 - http://1.bp.blogspot.com/_Gb4NmAd9gS8/TBi5f1T_mbI/AAAAAAAABEg/aj3tPWrLIM4/s1600/no03.jpgFigure 7.4 - http://projectsreview2010.aaschool.ac.uk/EMERGENT-TECHNOLOGIES/Tensegrity_Pavilion-fig03.jpgFigure 8.0 - https://www.draw.io/Figure 8.1 - https://www.draw.io/Figure 8.2 - https://lh6.ggpht.com/unEy5wL69aghKMSQa5T3ynaK51G3tlo3DSIpDkGk9oFfY1Y43wHFH-SFigure 8.3 - http://www.designboom.com/wp-content/uploads/2014/08/iaac-translated-geometries-designboom-11.jpgFigure 8.4 - http://photos1.blogger.com/blogger/7184/598/1600/08.jpgFigure 9.0 - http://pr2014.aaschool.ac.uk/submission/uploaded_files/EMERGENT-TECHNOLOGIES/Bootcamp-BC3_black.jpgFigure 9.1 - http://sustainability-certification.com/wp-content/uploads/foto-cg-rainwater.jpgFigure 9.2 - http://www.hydrothrift.com/images/coolingsystem/CW/chiller02.jpg.Figure 9.3 - http://hightech.lbl.gov/benchmarking-guides/images/clean-t3-fig1.pngFigure 9.4 - http://www.pdl.cmu.edu/DCO/p7ssm_img_1/fullsize/chilledwaterloop.jpg


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Figure 10.0 - http://api.ning.com/files/*3ORy5U8wM4YwGsVuGYkr*ygVr-/ParametricSpace.pngFigure 10.1 - http://www.currentresults.com/Weather-Extremes/US/hottest-cities.phpFigure 10.2 - http://www.currentresults.com/Weather-Extremes/US/hottest-cities.phpFigure 10.3 - http://www.arizona-leisure.com/phoenix-area-map.htmlFigure 10.4 - http://images.climate-data.org/location/1468/temperature-graph.pngFigure 10.5 - http://images.climate-data.org/location/1468/climate-graph.pngFigure 10.6 - http://www.nationsonline.org/oneworld/map/google_map_Phoenix.htmFigure 11.0 - http://digitalsubstance.files.wordpress.com/2011/11/oraitolookalike03m.jpgFigure 12.0 - http://www.sean-madigan.com/2012/03/26/tessellate-and-extrude/`

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