3-1 . Introduction
3-2 . Philosophy
These component-based and bio-inspired designs have been advocated by Michael Weinstock since the 90’s. 'Fitness Criteria' is the key word used by Weinstock to evaluate these design methodologies. These methodologies, increasingly popular in architectural research especially in the United Kingdom and the United States, have happened because of development of computer aided calculations and design. However, these design methods have existed in traditional forms as well. Architecture has a fixed scale as the human body size is immutable creating a basic component module. In addition, component size is always constrained for convenience of construction.
In contrast to these parametric systems, the developed component system is kinetic and thus morphological differentiation is not needed as differentiation occurs through the programmable aspect of the architecture. Due to their visual similarities, frequently PA (programmable architecture) is wrongly taken as Parametric Architecture. These two approaches are slightly different, both at the design system level as well as in their physical modeling (architecture is used here to refer to both a system and a building).
The methodology used in Parametric Architecture consists, firstly in preparing the original components like ’the induced pluripotent stem cells’ (iPS cell) and the global shape, then the component is morphed in order to adapt itself locally to the global shape. In contrast in PA, the global final result is more than the resulting morphing and distribution of the components along the global shape. The components are always identical, reacting locally to the global shape deformations. Parametric Architectural methodology has only been recently developed with the availability of computer aided design software (CAD).
Thanks to the increasing variety of fabrication technologies, a greater variation of shapes can also be materialized. This parallels the 'differentiated' shapes one finds coming from a single component one finds in the biological realm. Unlike in nature, however, these deformed shapes don’t have unique functions. Thus while it is true that Parametric methodology enables free form design and production, it is difficult to identify any other 'architectural' usefulness apart from the architects’ desire to make unique shapes.
3-2-2 . Ubiquitous Architecture
Both alone and with PARC Director and Chief Scientist John Seely Brown, Weiser wrote some of the earliest papers on the subject, largely defining it and sketching out its major concerns (Weiser, 1991) (Weiser, 1996) .
The second aspect is pervasive computing. At their core, all models of ubiquitous computing share a vision of small, inexpensive, robust networked processing devices, distributed at all scales throughout everyday life and generally turned to distinctly common-place ends. For example, a domestic ubiquitous computing environment might interconnect lighting and environmental controls with personal biometric monitors woven into clothing so that illumination and heating conditions in a room might be modulated, continuously and imperceptibly. Another common scenario posits refrigerators “aware” of their suitably tagged contents, able to both plan a variety of menus from the food actually on hand, and warn users of stale or spoiled food. (Refer from Wikipedia) (Sakamura, 2007) .
Cooperative research within the architectural field could be taking place in several organisations. Firstly, Dr. K.Sakamura of University of Tokyo, is to enable any everyday device to broadcast and receive information (Sakamura, 2007) .Secondly, MIT has also contributed significant research in this field, notably Things That Think consortium (directed by Hiroshi Ishii, Joseph A. Paradiso and Rosalind Picard) at the Media Lab and the CSAIL(MIT Computer Science and Artificial Intelligence Laboratory) effort known as Project Oxygen (MIT-Media-Lab, 2007) .
In this project architecture is defined as an intelligent machine that integrates a number of computers dedicated to sensing-calculating-actuating, capable of decision-making in order to produce an interactive interface. The proposal uses built-in computational systems such as various kinds of sensors (especially in this thesis light sensor), RFID tag technology (Radio-frequency identification (RFID) is a technology that uses radio waves to transfer data from an electronic tag, called RFID tag or label, attached to an object, through a reader for the purpose of identifying and tracking the object.) behind walls to make architecture interactive.
These technologies are used not only for sensing inputs, but also as evaluators to decide the effectiveness of this architectural system to implement a ‘feedback loop’ . Several study methods in this thesis will examine how kinetic architecture can be more efficient than the static architecture, how to control the architectural machine in order to provide kinetic-interactive architecture. (For example below the illumination experiment’s detail is described) The tools used, especially Arduino (hardware) and Processing (software) will be discussed.
The aim is to have built-in computational flexibility in the building in the first instance, so that the system can achieve any required change through software update. It will be necessary to think more about the appropriate type of technologies for the four hierarchical levels defined earlier. Is the same kind of technology going to be used in each of the four levels? Otherwise, what functions would we like the Room to have and how are these going to “inform” the higher levels ?
3-2-3 . Programmable Matter for Architecture
For example if a designer wants to use fabric for cloth, its grid should be smaller than a person’s scale and if it wants to keep ordinary functions, such as air for insulation, this homogeneous grid should be smaller than roughly 10mm to avoid circulation of the air.
Hence, in the architectural field, ‘programmable matter’ has to have architectural meaning, in particular here, this proposal will take a robotic kinetic methodology which means this project fits below the modular robotics approach, a typical component scale is roughly defined by a 100mm*100mm*100mm three dimensional grid, and the whole fabric will be 100m*100m to cover an urban patch as a fabric, because of its architectural function = as a structure which can integrate people.
As an estimated issues are below. Firstly, its actuator will be affected critically depending on scale. In the case of the Okayama competition, the actuator was an artificial muscle which was a kind of shape memory alloy. The strength of this muscle was strong enough for the 1/10 model while the strength was not strong enough for the actual 1/1 scale and there was no thicker wire. This resulted in a hold on this project. Secondly, a user (a human) already has his own scale; 1.7m height and his hand’s length is 40cm , but they cannot catch the earth or an atom. If this proposed fabric is used for furniture, I would think the component scale would need to be smaller than man’s body.
3-3. Engineering Tools
The proposed model had some points, but the fabric could not hold its shape by itself without electricity. It was trouble. So then referring from Tristan d’Estree Sterk ‘s actuated tensegrity components, (from his thesis Using Actuated Tensegrity Structures to Produce a Responsive Architecture) , got idea to make the fabric more stable using another vertical member, in his thesis, it is used as an ‘actuator‘, but in here the vertical member is replaced just as a shape supporter because we already have actuator as a tension member, the point is we needs keep. In addition, this proposal will add a different actuator and more smart system using Arduino.
- Characteristic of the selected shape memory alloy
Smart “NiTi” Spring (Manufacturer: RVFM) has been selected as an actuator of this thesis’ physical model. It is a 5.5mm external diameter spring made from Nickel Titanium alloy which has been heat treated to provide memory behaviour. Length is 20mm when closed and weight is 1.1g. From the manufacturer's product description, at room temperature the spring is soft enough to pull out to approximately 150mm by applying a small amount of force. When heated to 70°C by passing an electric current through it, the spring contracts to its original length with a pulling force equivalent to lifting a 0.5kg weight.
“The mechanism of actuation in shape memory alloys is a temperature-induced phase change which produces a significant shear strain on heating above the transformation temperature. This effect has given rise to a variety of applications (Duerig 1990). “(J. E. Huber, N. A. Fleck and M. F. Ashby, 1997)
Manufacturer’s technical data sheet gives the characteristic of the shape memory alloy spring. Overheating the spring may destroy the memory and may ignite adjacent materials so the electricity passed through the spring is to be kept not exceeding 3A. For example, a 6v lantern battery connected across the two ends of the extended spring will supply sufficient current for the spring to contract. The resistance of the spring is quite low, therefore the battery will discharge quickly if left connected. The spring is not suitable to be soldered therefore has to be joined mechanically to any leads. A terminal connecting block provides a simple connection when the two extremities of the spring are straightened.
A bias spring or mass may be used to extend the smart spring relaxed at room temperature. When electricity is applied, the contraction force of about 10 Newtons will overcome the bias and do any additional work for actuation. In this cycle the rate of relaxation is slower than contraction but can be speeded up by cooling (e.g. moving surrounding air with a fan).
- Highest power and displacement
fig 3-3-1-1,1: Actuation Stress, versus actuation strain for various actuators
- Cycle Operation
In my physical model a spring is used to reset the force. The movement of the actuator is measured, in part, by the level of stress one uses to reset the wire, or to stretch it in its low temperature phase. This opposing force, used to stretch the wire for cycle operation, is called the bias force. In my physical model, the bias force is exerted on the wire constantly by the spring, and on each cycle as the wire cools, this force elongates it. If no force is exerted as the wire cools, very little deformation or stretch occurs in the cool, room temperature state and correspondingly very little contraction occurs upon heating.
According to the manufacturer, up to a point the higher the load the higher the stroke. The strength of the wire, its pulling force and the bias force needed to stretch the wire back out are a function of the wire size or cross sectional area and can be measured in pounds per square inch or “psi”. If a load of 5,000 psi (34.5 MPa) is maintained during cooling, then about 3% memory strain will be obtained. At 10,000 psi ( 69 MPa), about 4% results, and with 15,000 psi (103 MPa) and above, nearly 5% is obtained.
fig 3-3-1-1,2: Normal Bias Spring
fig 3-3-1-1,3: Shape memory alloy (NiTI) in the model
3-3-1-2 How to Sense
fig 3-3-1-2,1: The Screenshot of Sensing with Arduino (K.Hotta)
fig 3-3-1-2,2: The Screenshot of Sensing with Arduino part2 (K.Hotta)
3-3-2 . Required Software
As a result they can achieve higher intelligence. An example of this program was prepared for Okayama-LRT competition 2010 by K.Hotta and A.Hotta. Using spring systems, the white circles (=agents) were continually changing their position depending on the weight, in other words, attracting and repelling forces. For example according to the table, the square and a parking space should be close because of traffic convenience while the bus stop and rest room should be distant from each other to prevent noise. In this program, a function is drawn as a white circle and its influences are visualised as lines which will converge at balance points. The spring system is simple yet it is able to optimize the problem (higher intelligence).
The second aspect of the software is the human interface. In processing, there is a famous plug-in library to make a GUI which is controllable by mouse, called ‘control-p5’. Using this tool, it is possible to make a convenient, beautiful interface to parametrically control the architecture which allows user participation in controlling the architecture’s character. It is possible to further develop this interface to be wireless by using touch OSC. (TouchOSC is a modular OSC and MIDI control surface for iPhone / iPod Touch / iPad. It supports sending and receiving Open Sound Control messages over a Wi-Fi network using the UDP protocol and supports both CoreMIDI and the Line 6 MIDI Mobilizer interfaces for sending and receiving MIDI messages.)
Third aspect of the software to be considered is an agent-based system for multiple user participation. Basically, inputs are made not only through environmental stimuli but through human participation as well. The environmental stimuli is measured via sensors while the human input is transferred via Internet and php protocols though the above interface technique.
fig 3-3-2-1,1: The Screenshot of Rhinocerous (K.Hotta)
fig 3-3-2-2,1: The Screenshot of Grasshopper (K.Hotta)
fig 3-3-2-3,1: The Screenshot of Galapagos(K.Hotta)
3-3-2-4. Kangaroo Physics
fig 3-3-2-4,1: The Screenshot of Kangaroo Physics (K.Hotta)
fig 3-3-2-5,1: The Screenshot of Processing (K.Hotta)
fig 3-3-2-6,1: The Screenshot of Arduino and ArduinoMega Board
3-3-2-7. Traer physics
3-3-3 . Brief Introduction and of Genetic Algorithm
3-3-3-1. History of Genetic Algorithm
3) In GA, even intermediate answers can be yielded at practically any time, because its run-time process is progressive. GA output is a never ending stream of answers, dissimilar to a number of dedicated algorithms. Though the newer answers tend to have higher quality, it can provide the answer nonetheless. So even a premature run can bring a harvest of sorts which could be called a result. This could be a great benefit to real time usage.
4) GA including its evolutionary solvers are manipulate-able by the user. There are a number of opportunities for interaction between algorithm and human: user or controller, because the run-time process is highly transparent and brows-able. It is important to note that the GA and its solver can be coached by human intelligence, even in the middle of its process, sometimes interrupting the process. Therefore it can be goaded into exploring not only normal optimum answers but also sub-optimum branches, if needed.
Evolutionary Algorithms including GA are slow; takes a long time to get a beneficial result, although it depends on the settings such as fitness pressure. Chiefly complicated setups that require a long time in order to solve a single iteration will quickly run out of hand, moreover it is not unheard of that a single process may run for more than a day. According to Davit Rutten, who is the creator of Galapagos it needs at least 50 generations of 50 individuals each, which is almost certainly an underestimate unless the problem has a very obvious solution. Then he would take a two-day runtime. For example this 4 components iteration needs 3 minutes to get an almost ultimate result.
3-3-3-3. The Basic GA Procedure
fig 3-3-3-3,1 Basic GA procedure
3-3-3-3-1. Step-1) Generate Initial Group
fig 3-3-3-3-2,1 Fitness landscape
However usually the number of genomes is more than two so then the actual field space will be more than 3 dimensions, which is difficult to show on paper, even on a 3 dimensional space. The common way to represent this space is to project into 3 dimensional spaces (such as fig 5-3-2). Every combination of genomes results in a specific point in space and indicates particular fitness, as high or low in Z direction index. The algorithm attempts to search the highest peak in this landscape, this is called optimum.
However, Instead of accurate discussion in biology, here ‘fitness’ is just focusing on the computational side. At least in Evolutionary Computation, fitness is a very easy concept. Fitness, also fitness function, is a particular type of objective function that is used to summarize, as a single figure of merit, is how close a given design solution is to achieving the set aims(Nelsona et al., 2009). In general terms, fitness is how close the user (here user means the person who is handling GA) wanted it to be. When the user is trying to solve a specific problem, and therefore he/she knows what it means to be fit. The fit individual has features as described below. The fitter individual can produce more offspring than the unfit one on average. So there is an interrelation between fitness and the number of offspring. Or, it is also a possible alternative to count the number of grand-offspring. And a better measure yet would be to count the allele frequency in the gene-pool of the genes that make up the individual in question.
fig 3-3-3-3-2,2 Climbing up fitness landscape
On the figure, colored circles represent the location of the ancestral genome, the line track represents the pathway of the offspring. Executing the algorithm a number of times is actually equal to interact genomes with landscape. By using a technique such as crossover, which is explained later, every genome climbs up the hill. Every peak in the fitness landscape has an upside down bowl of attraction around it. Then those surfaces turn to the valley. Some sections of this landscape represent the trace of genomes in model-space that will converge upon that specific peak of mountain or hill. The shape of this bowl or steep of the sloop is dependent on the way the user set a fitness function.
When the solving questionnaire is easy, the landscape may be a craggy mountain. In case of a difficult problem the landscape may be smooth and it may be difficult to find a hilltop. With this technique, in the case of a typically difficult question to solve, the solution tends to get stuck in local optima. However this sort of problematic fitness landscape could be manipulated by some techniques.
fig 3-3-3-3-2,3 Difficult Fitness Landscape
This immediately causes the extinction of the rest of the population and then the algorithm will miss the chance to find a higher peak on the right. The sharper the landscape for a solution, the harder it is to solve a problem with GA. Another difficult case is called ’discontinuous’ in the figure. Because most of the part consists of horizontal patches, there are no peaks. It causes difficulty in searching the usual way, thus there is continuous improvement through solver on this plateaus.
When the genome encounters this flat part, it will lose the compass and thus even after a few generations, nothing changes. This is equal to no fitness pressure. Until the genome comes across the upper plateaus accidentally, most of the dominating genes are meaningless ones. The final worst case is named as ‘Noise’ here shows a spiky landscape. Even though the solver might achieve the top of a spike, after making the crossover it may suddenly lose their fitness. The reason is that GA attempts to proceed by guessing the approximate right direction. But in this case they suddenly fall down into a crevasse. This sort of landscape makes this algorithm null.
fig 3-3-3-3-3,1 : The Diagram of Different Selection Methods
Exclusive Selection is the selection method in which only the top N% of the population gets to mate. N is arbitrarily numbered from 0 to 100. When the individual is within N%, he/she can get offspring or multiple offspring. This method obviously affects the gene-pool by improving their fitness, because only fit individuals can reproduce. It is possible to find instances of this in nature, that is Walrus males. Only a certain percent of males can have a harem, the flunkies’ just stay inside without any opportunity to breed.
Biased Selection, which is when the chance of mating increases as the fitness increases, is another common method in nature. This is something typically seen in species that form stable couples. Essentially everybody has the capability of finding a mate, but the more attractive individual has a higher probability of breeding, thus increasing their chances of becoming genetic founders for future generations. To manipulate and control the evolutional direction, power functions are sometimes used to amplify biased Selection. For example, when fitness is more important, exaggerate the curve by using multiplication with a number more than 1, otherwise on the other hand flattening it with less than 1.
There are several methods as a concrete algorithm. But here, ‘Roulette Wheel Selection’ is introduced as the most simple and common example. It is also widely known that the roulette method is not practical for some reasons, so when this is used several techniques such as ‘scaling’ or ‘tournament’ methods may also be implemented. This method is defined as selecting individuals following the fitness proportion. This is so named because this way is similar to the roulette on the darts game which has the fan-shaped target but here the target area relates to fitness.
fig 3-3-3-3-4,1 : The Diagram of Crossover
As Mendel discovered in the 1860′s, genes are not continuously variable qualities, but they behave like on-off switches. When Mendel crossed wrinkly and smooth peas, he ended up with specific frequencies of each in the subsequent generations, but it never revealed peas whose skins are somewhat wrinkled or smooth.
There are no gender or sex-based characteristics in the solver. So the combinations of two genes are potentially a completely symmetrical process. In Crossover mating, junior inherits some number of genes (there are a number of ways to exchange genes, such as one point crossover, multipoint crossover, uniform crossover) from one and the remainder from the other. Because of this mechanism gene value is maintained.
Blend Coalescence (including blending preference) is another method for coalescence. For this, new values are computed for genes instead of duplicating existing genes. The most simple example of logic is averaging the values, but others have biased percentages in those interpolations. The latter is called blending preference, usually used with relative parental fitness. When one parent is fitter than another, the former's gene will take a higher proportion in their offspring. This operation is quite natural to get fitter descendants. However, this is not entirely without precedent in biology, it depends to some extent on what level of scale you define as ‘gene’.
fig 3-3-3-3-5,1 : The Diagram of Mutation
3-3-3-3-6. Re-generation and Repetition
3-4 . Conclusion