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index

Introduction

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Introduction: Team Introduction: Keywords Introduction: Abstract Introduction: Brief Introduction: Material Introduction: ArtiďŹ cial Intelligence Introduction: Research Question

Production Method 1

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Method 1: Process Method 1: State of the art Method 1: Problems Method 2 Method 2: Process Method 2: State of the art Method 2: Improvements Method 2: Training

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Application

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Application: Vision Application: Prototype Application: Training

Pattern

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Pattern: Inuences Pattern: Optimization

Conclusions

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References

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introduction

introduction: Team Researchers: Ardeshir Talaei, Daniil Koshelyuk, Soroush Garivani Lead Faculty: Areti Markopoulou Faculty Assistants: David Andrés León, Raimund Krenmüller Computational Advisor: Angelos Chronis In collaboration with: Italian Institute of Technology, the Smart Materials Group IT Material Experts: Athanassia Athanassiou, Ilker Bayer, Giovanni Perotto, Material Scientist

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IAAC

Digital Matter Studio

2018

introduction: Keywords responsive architecture, material intelligence, buildings that think,adaptive structures, artiďŹ cial intelligence, digital fabrication, graphene, material system, membrane, sensing, adaptation

introduction: Abstract Presented research exlpores possibilities of graphene material systems enhanced with artiďŹ cial intelligence algorythms in context of smart architectural elements addressing performative augmentation of structural and environmental behaviour, in particular focusing on scenarios envolving wind actuation.

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introduction: Brief As we transition into an Information Era physical spaces become entwined with digital layers, new fabrication technologies introduce new possibilities but also uncover new challenges. Moreover, in this state of constant change and evolution our environment remained surprisingly static and unwieldy.

Coming from industrialization phase through ecological mindfulness phase, fabrication techniques grew from focus on mass production to smart and more selective application with respect to long-term consequences. However, lately with spread of 3D printing and with extensive research into material science we come to a point where no longer it is more expensive to introduce variability. In fact, locally adapted and optimized solutions have proven to not

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only perform better but also provide meaningful feedback throughout lifecycle.

On the other hand, as computational power becomes smaller and more accessible with systems like Arduino driven microcomputers, we start to have access to a new layers of data to inform our design towards better solutions. Sensing capabilities are now embedded to the material systems: material, sensor, actuator in one synergetic ecology. Addressing this potential in a conscious way is one of the most important goals of new architecture.

At the same time, as our environment gets more and more autonomy and inherent intelligence they have to serve the people that inhabit it. More options for automation does not

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necessarily mean more interaction.

This interaction is now as important part of design as functionality and performance.

In this framework, studio focuses on research and development of material systems for built environments that can enhance or augment the space we live in. By inspecting possibilities from material physical and chemical qualities to full-scale prototyping, coupled studio aims to create dynamic and live architecture.

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introduction: Material While often research is driven from certain need to the solution, in our case we started from a material system we aimed to explore. Graphene is a unique material with incredible properties. A single-molecule layer hexagonal grid of Carbone atoms has been envisioned for a long time to be possible. Recent break throughs in making it physically uncovered not only chemical properties it was thought of initially, but surprising physical qualities as well. The geometry of the structures allows for fait bit of exibility maintaining unmatched force of intermolecular bonds. The conďŹ guration and chemical composition not only allows for regular grid electron transference but transitions due to free connections of all the atoms. This means extremely high heat and electrical conductivity. Moreover, one molecule

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sheet structure also provides with unique border properties. Potentially the material is transparent as well.

Of all the qualities of this wonder-material, we focused on electrical conductivity. There are three ways to beneďŹ t from it: with graphene embedded mixes, with compressed nano-platelets and ultimately with the pure sheet itself, though this is outside of the scope of possibilities of the lab at current time. In the ďŹ rst case, the changes in conductivity are primarily driven by micro-cracking of the host base material. With the second method however main difference comes from relevant displacement of nano-platelets themselves.

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introduction: Artificial Intelligence Science of artificial intelligence has been gradually developing since 1960 by researchers like Marvin Minskiy, John McCarthy, Allen Newell [4]. Herbert Simon, Arthur Samuel.

Original hypothesis was that simulating behavior of our intelligence computers would be able to learn to solve same kinds of problems we can but faster. However, experience has proven that in fact an opposite is true. In fact, the term learning has proven to be much more difficult to even formulate. Learning on a limited set of example data means you don’t only have to be able to be good at noticing patterns but at the same time you have to be capable of generalizing – two contradictory skills you have to balance.

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Apart from purely computational applications (search engines, spam filtering, voice recognition and generation with assistance), the technology has been actively spreading over last years into various fields.

For instance, the Watson system has already revolutionized medical industry, various solutions has been implemented in self-driving cars, finances or even in advertisement with behavioral prediction models. In architecture the applications of Artificial Intelligence has been extremely limited. Since most of current architecture is static, applications mostly focus on initial design stages (WeWork office designs, projects of The Living) and smaller scale prototypes of material systems in academic environments.

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The strength of AI based systems are when applied to massive sets of data, or with multidimensional non-linear complex patterns that would require extensive simulation processes, that is not possible most of the time, or much too speciďŹ c algorithmic expert-solutions that have proven to be not exible with scale or changes.

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pic. 1 classic neural network scheme

pic. 2 decision tree classiďŹ cation example

pic. 3 random forest algoythm scheme

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introduction: Research Question

With these two emerging technologies in architecture as a premise for the research the initial question we formulated was:

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“How can electrical responses of [graphene material systems] combined with [artificial intelligence], enable us to create [intelligent architectural components]?”

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production Theoretical qualities of graphene are truly amazing. However, practically a lot of methods and techniques are in earliest stages of scientiďŹ c research and thus are not available for industrial of prototyping purposes. This led to our focus on physical testing of potential methodologies and later exploring space of potential. As such, the main stage of our research was centered on testing two major avenues of explorations and subsequent improvements and alterations to the material systems.

method 1

method 1: Process As initial strategy, we chose to test a graphene mixed ink to see if we could produce electrically conductive

pic. 1 conductivity test mix 20% graphene, 80% polyurethane all strips are 10 cm in length and resistance values are in kΊ.

patterns on a deformable base material.

The liquid component for the ink was narrowed down to polyurethane due to its elastic properties in set state.

pic. 2 conductivity test mix 10% graphene, 90% polyurethane.

The base material was chosen to be thermal plastic sheets for its plasticity and potential of deformation.

From mixes of 5%, 10% and 20% on strips of 100 mm of length and of 25 mm width, the 10% displayed [less noticeable wear, more stable reading values and more suitable range of resistance].

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pic. 3 conductivity test mix 5% graphene, 95% polyurethane.

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method 1: State of the art 1.

Jam Sheets

Jifei Ou, Lining Yao, Daniel Tauber, Juergen Steimle, Ryuma Niiyama, Hiroshi Ishii / 2014

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Graphene Composite

Javier Lรณpez, Alascio Hervรกs , Gelder Van Limburg Stirum, Ricardo Mayor Luque, Thanos Zervos / 2017

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method 1: Problems This material system though showing potentials had inherit limitations.

First of all, upon use the material was subject to fast wear becoming unusable. Secondly, end state of the ink was solid thus; the changes from resistance were caused by micro-fracturing of the material. This meant that on deformations bigger than [] the system as well was becoming unusable.

Lastly, the types of deformations detectable were not only limited to bending and touch were only possible to be planar at any speciďŹ c time of interaction. This meant that the range of detectable external stimuli was potentially simulatable.

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method 2

method 2: Process Alternatively, more exible base materials in term required alternative approach for base of the conductive layer. Testing various fabrics previously used in architectural context, we selected silicone. It does not just provide impressive stretching possibilities, but is relatively strong; it is water-resistant and powder impenetrable, potentially transparent and industrially produsable in large-scale applications.

This however, requires more exible conductivity method. Not only does most liquid graphene inks do not apply easily to the silicone, the degree of local deformations is far bigger.

Drawing inspiration from the ďŹ eld of wearable sensors using same typology of material behavior, we tested nano-platelet encapsulation method.

The process so far evolved to hav-

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ing a solid sheet of silicone, on to which [a liquid silicone is applied in the shape of required pattern, limited by the negative as a preliminary glue solution to ďŹ x it in place for application process]. On top of that went a graphene powder layer, pressed by the positive pattern mold. Then in the needed reading and writing points we applied the metallic pins that than were ďŹ xed in place with the graphene layer as well by casting over the whole surface liquid silicone solution.

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method 2: State of the art

1.

Strain Sensors Stretchable

Conductors Using Graphene / Silicon Rubber Composites Ge Shi, Zhiheng Zhao, Jing-Hong Pai, et al / 2016

2.

Recognizing Upper Body Postures using Textile Strain Sensors Corinne Mattmann, Frank Clemens and Gerhard Trรถster // ETH Wearable Computing lab 2008

3.

PROSKIN

Ingried Ramirez, Robert Staples, Burak Paksoy, Chenthur Raaghav / 2016

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method 2: Improvements The issues that arose with scaling were: consistency of readings through similar patterns and over time, integrity of pressed graphene layer through positive mold removal and through layer ďŹ xation.

Most reliable method for reading data appeared from applying L-shaped metallic plates over pressed graphene before ďŹ xing the layer. This way the thickness of graphene is universal and unaffected, the pins are easier to deposit without risk of damaging the pattern and relative to the platelets we discovered minimal disposition upon stretch or use.

Second noticeable inuence on the quality of the graphene nano-platelets strip was the amount of pressure you apply to a surface. As discovered in [1] in order to maximize

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conductive qualities of platelets they have to be compressed. However, too much pressure seemed to have made the pattern strips more brittle.

Without having equipment for measuring the pressure, we estimate the best behavior in range [2].

Moreover, the best quality of the pattern was achieved when negative mold was held on with pressure as well not allowing platelets spread under upon pressing.

Thickness of the pattern also appeared to play an important role in maintaining structure of the pattern before securing it with silicone. More speciďŹ cally excess millimeters of deposited powder before compression lead to bigger momentum on top layer of pattern and thus making it more brittle. Ratio of height to

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width of the mold chamber used for the most successful prototype was 2mm:6mm being too unstable already at 4mm:5mm.

As it comes to minimizing risks of cracking the pattern upon removal of the molds were designed to slide out as easy as possible making it easier on a bigger scale to remove without affecting the pattern and with smaller percentage of powder form contaminate free space of the membrane.

Silicone deposition was a crucial part of the process since it was chambering all the pieces together. However, being viscous as it is the process is prone to premature setting and thus dragging of the pattern and uneven application of the material.

Attempt of using a complete sheet

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with a thin layer of evenly spread liquid silicone as a glue between

layers proven to introduce too much of the local forces on top layers of the pattern as well as signiďŹ cantly decreasing ability to stretch resulting membrane.

Introducing liquid silicone over a smaller area just surrounding and including the pattern proven useful for ďŹ rst capture but not enough on its own. The difference in properties of liquid silicone and silicone base sheet as well as shrinkage process in silicone setting caused the surface to become wrinkled. In the resulting methodology, this was immediately followed by more rough overall surface coverage.

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method 2: Training training Strategy At this point it made sense to start introducing Artificial intelligence into the system. Initial strategy selected to function as a proof of concept was to train to distinguish single point of force application and its force with the surface fixed to flat stretched state.

In order to provide controllable data set we designed a testing device using ABB manipulator to apply pressure at specific points on the surface, and signaling to collect the data. Then the current has been sent through the control points of the pattern in order and the numbers were batched into series to avoid overfitting to specific deformation cases.

Thus with information from the robot about location and the force and with

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readings from the pattern we drew example set for machine learning algorithms. The surface was trained in random points with random force applied and ultimately trained on regression algorythm to interpolate between 30,000 data points.

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training Conclusions Though the time constraints didn’t

pic. 1 data visualization normalized and stepped from patten with 4 read-points.

allow for more extensive data-collection process necessary for reliable prediction and correlation, the results of estimations were accurate 92% in classiďŹ cation and 80% on a regression model.

pic. 2 data visualization of readings in one speciďŹ c shape

Moreover, gathered data allowed to get valuable insights into the behaviour of the membrane and possible alterations such as: pattern on the membrane has unique relation with the sensetivity and thus with the accuracy of the prototype (see 0.1 and 0.9 value accurcay of predictions of the two algorythms on pic. 3).

Additionally, the more uniform is the quality of the pattern (the more self similar are correlating parts of the pattern) the less noise appears in the readings (pic. 1)

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pic. 3 comparison of predictions and real values of the position of the touch as result of two different neural networks..

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application

application: Vision Though currently architecture is just beginning to enter in the realm of dynamic structures, programmable materials and digital systems, uses of shape-sensing textile membrane are vast. More than that, many applications come from adjacent ďŹ elds.

From medical sensors to wearables, from wind maximizing sails to wind-minimizing tents, from pavilions to façade membranes, from AR/ VR interfaces to interactive surfaces.

Most of current solutions for incorporation of technologies in performative aspects of architecture follow a paradigm of technical add-ons.

Instead of populating existing space with complex devices we wanted to explore possibility of the material that is itself both performing and sensing and, ultimately, reacting.

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Thus, the objectives for the potential application – primary utilization of stretched state, relevance of local sensing capabilities, inherent unpredictability of geometries. All that makes it unfeasible or impractical if not impossible to simulate.

In our project of all possibilities we decided to focus on performative augmentation of structures with the main actuating force from environment - wind.

Such system could be applied in various scenarios: modular facade system augmenting ventilation through the windows, dynamic skin of a pavilion maintaining given shape despite the inuence of the elements, street adaptable tent structure aerodynamically chanelling air ows in the urban environment, etc.

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application: Prototype To demonstrate mechanism and capability of the material system we propose following prototype. The surface is ďŹ xed in diagonal corner points, opposite points are linked to vertically moving rail powered by motor. Trained surface is placed in tension in a base position thus is sensetive to deformation in any reactive state.

The input/output points are connected to the Arduino-nano board that processes and sends signals through the membrane circuit.

Upon detection of one of the trained states the surface [tightens itself].

Ultimately with more training positions and more control points including not corner points the surface could follow objectives like creating

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contolled temporary opennings or closings, maintaining closest possible shape to a certain task, break down ows creating micro-pertuberations, etc. - all actuated in a same way.

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application: Training In the case of deforming by wind we needed to create steady enough and strong enough load of wind to provide much slower Arduino processor to collect data. We equipped the wind motor with 3D printed robot gripper and channelling nozzel. Additionally we increased sensitivity by generating pattern by the prototype conďŹ guration and introducing 8 read/ write pins providing thus 49 data points per reading.

For the demonstration purposes the training dataset included [4] fan positions with a physical noise (with variations around set positions) to avoid prediction overďŹ tting.

Example data to relate to the electrical current data was than gathered by kinect sensor reading color-coded points on the surface

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pattern

pattern: Inuences Summorizing conclusions from various experiments with both patterns and prototype systems, we destilled following key geometrical points as major inuences on the prformance of the pattern: -

production capabilities: discon-

nected sections of mold add risk of breakage; -

amount and positions of read/

write points; length of point to point pattern sections; avoidance of dead-end without read/write points.

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pattern: Optimization While algorythm with an extensive enough set of example data is capable of estimatng the shape to a certain degree of accuracy, speciďŹ c deformation and prototype conďŹ guration will perform best with an optimised pattern.

Overall, the objectives are uniform coverage of inluence on the surface by the range of expected deformation forces, and at the same time maximization of differences between unique points.

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results

conclusions We started from two seemingly unrelated technologies, graphene material system and artiďŹ cial intelligence algorythms. Both are remarkable in their properties and in their application prospects. However truly remarkable behaviour comes from synergetic effect when you combine those. *** Material innovations historically have been related with development of architecture and now we are getting to absolutely new stage, when it is possible to synthesise the material acording to the needs and fabricate almost in any geometry.

In addition, not only can we now simulate, collect, process, visualize, store and transmit immense ammounts

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of data, - new layers of architecture become introduced with spread of digital technologies - hidden intelligence, internet of things, augmented reality, etc.

Graphene provided unique possibility to introduce a level of abstraction to the functionality we are trying to add to the spaces we live in. This new typology of architectural elements including various additional capabilities in a passive but significant way (without significant influence on its primary functionality) simplifies process of space development - instead of having a separate systems unrolled through the city with a whole new maintanance requirements involvent, a distributed network of self-reliant elements only requiering user inputs for objectives or task clusters.

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Built environment deal with external forces constantly, sheltering us from the elements, while various mechanical syhstems perfect microconditions of the artificial spaces. A dynamic skin system that could sense and chanel flow of air based on the force and user objectives could create far less energy demanding and far more accurate and local control over inner state. *** Future steps in the research include fabrication automation, extensive data collection and subsequent training, experimentation with various sources of deformation, prototype development in other scenarios, application specific reaction development.

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keep going ...

DEVELOPED [MATERIAL SYSTEM] ALLOWS FOR EMBEDDING OF [SHAPESENSING ] CAPABILITIES IN THE FUNCTIONAL [SKIN SURFACES] OF THE BUILT ENVIRONMENTS AND PROVIDES POTENTIAL TO PURSUE SPECIFIC [GEOMETRIC OBJECTIVES].

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references 1.

Xiaowen Yu, Huhu Cheng, Miao

Zhang et.al “Graphene-based smart materials” 2017; 2.

Corinne Mattmann, Frank

Clemens and Gerhard Tröster “Recognizing Upper Body Postures using Textile Strain Sensors” Wearable Computing lab, ETH 2008; 3.

Nilsson, Nils. The Quest for

Artificial Intelligence: A History of Ideas and Achievements. New York: Cambridge University Press 2009; 4.

Sang-Hoon Baea Youngbin Leea Bhupendra K. Sharmaa HakJoo Leeb Jae-Hyun Kimb Jong-Hyun Ahn, Graphene-based transparent strain sensor, Carbon, 2013; 5.

A. Sakhaee-Poura M. T. Ahmadiana A. Vafaib , Potential application of single-layered graphene sheet as strain sensor, Solid State Communications, 2008;

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6.

He Tian, Yi Shu, Ya-Long Cui,

Wen-Tian Mi, Yi Yang, Dan Xie and Tian-Ling Ren, Scalable fabrication of high-performance and exible graphene strain sensors, Nanoscale, 2014; 7.

Xuejun Xie, Hua Bai, Gaoquan Shib and Liangti Qu, - Load-tolerant, highly strain-responsive graphene sheets, Journal of Materials Chemistry, 2011; 8.

Xu Liu, Chen Tang, Xiaohan Du, Shuai Xiong, Siyuan Xi, et al - A highly sensitive graphene woven fabric strain sensor for wearable wireless musical instruments, Materials Horizons, 2017;

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alive

Presented research explores possibilities of graphene material systems enhanced with artiďŹ cial intelligence algorithms in context of smart architectural elements addressing performable augmentation of structural and environmental behaviour, in particular focusing on scenarios involving wind actuation.

IAAC

Digital Matter Studio

2018