EN_research: Optimization of house morphology to minimize energy demand using parametric tools

University of Thessaly Architecture Department

Research topic Optimization of house morphology to minimize energy demand using parametric and algorithmic tools

Pantelaiou Evangelia supervising professor: Tsangrassoulis Aris

Table of Contents 1.Terminology .................................................................................................... 7 2.Introduction .................................................................................................... 9 3.Research Objective .................................................................................... 14 4.Software ........................................................................................................ 15 4.1.Grasshopper .......................................................................................... 15 4.2.Ladybug and Honeybee ..................................................................... 18 4.3.Genetic Algorithms ............................................................................... 20 4.4.Galapagos ............................................................................................. 23 4.4.1.The Process ...................................................................................... 25 4.4.2.Fitness Function ............................................................................... 28 4.4.3. Selection Mechanism ................................................................... 32 4.4.4. Coupling Algorithm ....................................................................... 34 4.4.5. Coalescence Algorithm ............................................................... 39 4.4.6. Mutation Factories ........................................................................ 41 5.Examples ....................................................................................................... 45 6.Case Study ................................................................................................... 56 6.1.Given Data ............................................................................................ 56 6.2.Methodology ......................................................................................... 62 6.3.Results ..................................................................................................... 64

6.3.1.Experiment 1: Athens, 10% ............................................................ 65 6.3.2.Experiment 2: Thessaloniki, 10% .................................................... 66 6.3.3.Experiment 3: Athens, 20% ............................................................ 67 6.3.4.Experiment 4: Thessaloniki, 20% .................................................... 68 7.Conclusions .................................................................................................. 70 7.1.Disadvantages ...................................................................................... 70 7.2.Advantages ........................................................................................... 72 8.Epilogue ........................................................................................................ 74 9.Figures ........................................................................................................... 77 10.Bibliography ............................................................................................... 82

1.Terminology

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1.Terminology Conceptual design The initial stage of architectural design, the phase in which the most important decisions are made Parametric design The design that allows the expression of parameters and rules that together define, encode and clarify the relationship between design and design reaction1 Genetic Algorithm A method of finding optimal solutions in systems that can be described as a mathematical problem2 Shell morphology All elements that constitute the shell of a structure

Computer Aided Design (CAD) systems The use of computer systems to help create, modify, analyze, or optimize a design3 Rhinoceros 3D Commercial 3D Design Software Application with Computer (CAD)4 Grasshopper Visual programming language and environment running within the Rhinoceros 3D application5

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Parametric design, https://en.wikipedia.org/wiki/Parametric_design Genetic Algorithms, https://el.wikipedia.org/wiki/genetic_algorithms 3 Computer Aided Design (CAD), https://en.wikipedia.org/wiki/Computer-aided_design 4 Rhinoceros 3D, https://en.wikipedia.org/wiki/Rhinoceros_3D 5 Grasshopper 3D, https://en.wikipedia.org/wiki/Grasshopper_3D 2

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1.Terminology

Ladybug & Honeybee Collection of free computer applications that support bioclimatic design and training and work through Grasshopper6 Galapagos Genetic Algorithm that works through the Grasshopper

Gene Variable value accepted by the Galapagos genetic algorithm Genome Combination of two or more genes Fitness A value accepted by Galapagos and defined as maximum or minimum

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Ladybug Tools, https://www.grasshopper3d.com/group/ladybug

2.Introduction

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2.Introduction The need to include sustainable design strategies among the major design parameters in building projects of all scales seems to be imperative nowadays. Bioclimatic design methods lead to buildings that respond to the climatic conditions of their environment, are able to modify them and thus contribute to resource conservation, while optimizing climatic comfort.

The morphology of the shell of a building greatly affects energy demand and consumption. To minimize energy consumption, architects and engineers have two choices: either using energy-optimized materials, thereby increasing the cost of construction, or designing better the shape of the building shell in the early stages of the architectural design.

Without questioning the benefits of advanced building materials, the integration of energy-saving strategies by improving the geometric features of the building may prove to be a more effective technique the architect can follow, also justifying the role he has to play in the design process.

The shape of the building is usually defined in the early stages of the design, most of which are likely to undergo minimal changes until the end of the design process. Energy consumption figures are hardly ever calculated in the early stages, as the calculation of energy consumption is a time-consuming task and minimum energy efficiency rules are used to enhance the shell at this stage. Although important and useful, these are only general guidelines, often inadequate in more complex projects.

Buildings have long life cycles and are big energy consumers and the

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2.Introduction

shape of the shell plays an important role in energy efficiency. Therefore, it is wise not to depend solely on these basic bioclimatic tactics to ensure energy efficiency.

In the initial phase of the building design process the boundaries of the final result are not defined. Several studies have shown that many parameters that affect the whole cycle of the manufacturing process are identified in the initial phase of a project. A survey conducted by Guillemin and Morel7 found that 57% of the study decisions in 67 buildings were made in the conceptual stage and only 13% at the detailed planning stage. During design, project engineers participate in the production of a strategy to better match customer requests. Often, in larger-scale projects, it is the responsibility of the architect to link all aspects of the design to meet the client's criteria. Goals can be difficult to accomplish, as some parameters are more difficult to measure than others and are also very difficult to combine with different goals in the same analysis. Exploring the design space in the conceptual phase of a project is important, as "even the highest level of detailed design can not compensate for the bad decisions made in the conceptual stage"8.

At the beginning of the design of a building, architects and engineers face many economic, environmental, technical and aesthetic constraints. Indeed, a building is a very complex object and almost always unique. In addition, concern about energy saving and pollution 7

Guillemin, A., & Morel, N. (2001). An innovative lighting controller integrated in a self-adaptive building control system. Energy and Buildings, 33(5), 477-487.

8 Chong, Y. T. (2009). A heuristic-based approach to conceptual design. Research in Engineering Design, 20(2), 97-116.

2.Introduction

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reduction must remain principles of architectural design. The tools they use must be easy and allow their work to be directed to good energy solutions.

Over the last few decades, new design methods have come to the forefront of the architectural process that provides developers with tools to help them better understand the geometric boundaries and how they play a key role in the energy impact of construction. These technological developments, coupled with the ever-growing interest in ecological and financial requirements, have created new trends in architecture that are directed to design methods that are highly dependent on computing tools. Therefore, design aids, which were once merely supportive of the architectural process, evolved into essential elements of design, even in the initial phase9.

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Fasoulaki E. (2007) Genetic Algorithms in Architecture: a Necessity or a Trend?, Department of Architecture, Massachusetts Institute of Technology

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2.Introduction

Figure 1. Frequency of search for parametric modeling and parametric design terms in Google's search engine

Experimenting on building design with the impact of environmental parameters is greatly facilitated by CAD (Computer Aided Design) tools in architectural research and practice. These advanced systems incorporating computational tools, such as parametric design systems, make it possible to interact between the geometric shape of the building and the physical or other parameters. Such computational tools are those associated with Genetic Algorithms. The Grasshopper plugin, designed for Rhinoceros 3D, is ground for experimentation for any interested architect, and in combination with environmental tools such as Ladybug and Honeybee based on valid platforms, it can become a major planning aid that focuses on performance.

Within this new field of building design research, architecture explores new areas of building shell solving with the help of computational tools that create simulation environments that mimic the natural phenomena that affect the architectural form. Such simulation environments allow

2.Introduction

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the integration of environmental parameters and requirements into the planning process and enable experimentation with sustainable design strategies that can lead to new and interesting building styles.

In this respect, the introduction of sustainable methods and strategies used in architecture coupled with fast-growing genetic and parametric design can address current sustainability needs and eventually shape the future face of the urban landscape.

Suburban locations as well as campuses, which are smaller in scale and inherently more open to innovation at all levels, can be more easily used as a test bed for experimental approaches before these techniques are implemented in real urban areas.

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3.Research Objective

3.Research Objective The present research attempts to examine whether a Genetic Algorithm such as Galapagos can define basic geometric elements of the shell of construction with the aim of reducing energy consumption and providing solutions that are functional and aesthetically acceptable.

Familiarization with parametric design tools, as well as a better understanding of the effect of the shell shape and morphology on its energy performance are also important results of this research.

As far as the literature is concerned, the sources are limited as it is a relatively new field of architectural research. Most of them relate to conceptual analysis of parametric design and design using genetic algorithms, the majority of which come from architects. Practical studies with concrete examples come from civilian or mechanical engineers and relate to relevant elements. Few of them are related to energy factors and even from the perspective of the architect. However, there are unlimited tutorials on the use of computer tools (Grasshopper, Galapagos, Ladybug & Honeybee) and various forums that are an important help in this endeavor.

4.Software

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4.Software In 2013, Jabi defined parametric design as "an algorithmic thinking process that allows the expression of parameters and rules that together define, codify and clarify the relationship between design intent and design response" 10. Parametric or genetic or algorithmic design is mainly an effective way of flexibility for the designer.

Genetic algorithms do not solve the problem in an analytical / mathematical way but with a biological one. They therefore have greater endogenous flexibility and freedom to choose a desirable optimal solution according to the specification of the problem.

Essentially, genetic algorithms are search algorithms (heuristics) that seek to solve the problem we assign them11.

Below is a brief description of the software used in the research work.

4.1.Grasshopper Grasshopper is a visual language and programming environment developed by David Rutten, which works through Rhinoceros 3D design software. The plugin works by transferring components to a canvas whose inputs receive information of various types (numeric data,

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Jabi W. (2013) Parametric Design for Architecture. London, Laurence King Publishing Ltd Nembrini J., Sambergera S., Labelle G. (2014) Parametric scripting for early design performance simulation, Energy and Buildings 68, 786–798 11

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geometric data, text data, etc.). The outputs to these elements are then connected to the inputs of the following elements. Each item can accept user-defined data on the Grasshopper interface, the Rhino interface, or files on the computer.

Figure 2.Grasshopper interface

In Figure 3 we see two parameters associated with an ablation component. The two yellow boxes on the left side define a set of numerical constants. The top panel contains four integer numbers (6, 7, 8 and 12), while the lower panel contains only one value. These parameters are related to the removal component, which results in four output values (6-5 = 1, 7-5 = 2, 8-5 = 3 and 12-5 = 7). The same result can be achieved by using text expressions as shown in Figure 4. In this way,

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Grasshopper allows users to combine both visual and textual programming within the same environment.

Figure 3

Figure 4

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4.2.Ladybug and Honeybee Ladybug and Honeybee are free, open-source applications that include environmental design tools based on valid simulation engines (Radiance, EnergyPlus / OpenStudio, Therm / Window and OpenFOAM).

They were created by a group of developers with founders Mostapha Sadeghipour Roudsari and Chris Mackey in 2013. Since then, many developers have contributed to the community in many ways. As a result of their efforts, the Ladybug and Honeybee tools have evolved into multiple interconnected libraries and plugins that are used in the academic world as well as in architectural and engineering offices around the world. They allow a wide range of analyzes and have changed the face of ecodesign of buildings through their application to projects12.

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Our Story, https://www.ladybug.tools/about

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Figure 5. Ladybug components

Figure 6.Honeybee components

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4.3.Genetic Algorithms A genetic algorithm is based on heuristic search13 inspired by Charles Darwin's theory of natural evolution. This algorithm reflects the process of natural selection where the most appropriate individuals are selected for reproduction to produce next-generation offspring: the process of natural selection begins with the selection of the most appropriate individuals from a population. They produce offspring that inherit the characteristics of the parents and will be added to the next generation.

If parents have better features, their offspring will be better off than parents and will be more likely to survive. This process continues and in the end, a generation is created with the most appropriate individuals.

This idea can be applied to a search problem. We consider a set of solutions for a problem and choose a percentage of the best ones.

The Genetic Algorithm process is described in five steps below. 14

• Initial population The process starts with a set of individual elements called a population.

Every such element is a solution to the problem we want to solve.

Heuristic (computer science), https://en.wikipedia.org/wiki/Heuristic_(computer_science) 14 Vijini Mallawaarachchi, Introduction to Genetic Algorithms - Including Example Code, https://towardsdatascience.com/introduction-to-genetic-algorithms-includingexample-code-e396e98d8bf3 13

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An element is characterized by a set of variables known as genes. The genes are joined in a series to form a chromosome that represents a solution to the problem.

In the language of genetic algorithms, the genes set of a genome is represented using a series of binary values (a string of 1 and 0) (Figure 7).

Figure 5

• Fitness The fitness function determines how appropriate a genome (the ability of a genome to compete with another). The probability that a genome will be selected for playback is based on the fitness value.

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•Selection This procedure is the selection of the most potent genomes. Two pairs of genomes are selected based on fitness rates. High fitness genomes are more likely to be selected for reproduction.

•Crossover This is the most important phase of a genetic algorithm. For each pair of genomes selected, an intersection point is selected randomly (Figure 8).

The offspring are created by exchanging the parents' genes together until the crossing point is reached (Figure 9). The offspring are added to the population (Figure 10).

Figure 8

Figure 9

Figure 10

• Mutation In some new offspring that are formed, some of their genes may undergo a mutation. This means that some of the string bits can be inverted (Figure 11).

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Figure 11

The mutation occurs to maintain diversity within the population and to prevent early convergence.

The algorithm ends when the population converges (it does not produce offspring that differ significantly from the previous generation). Then it is assumed that the genetic algorithm has given a set of solutions to our problem.

4.4.Galapagos This is a genetic algorithm that works as discussed above. Galapagos accepts multiple variable values and a value dependent on these, which is defined as minimum or maximum by the user, depending on the

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desired result. Figure 12 shows the Galapagos interface: the first graph is expressed in two axes X and Y: the X axis is the time or rather the generations, the Y axis is the fitness. The red line is the average fitness value for each generation. The yellow area links the weakest and the strongest genomes for each generation. The orange area represents the standard deviation from the average fitness. The [+] symbols at the top indicate that at least one person in generation X is more capable than the most capable person in the generation X-1. The three lower charts represent the pool of the genomes, the graphs of the genes that make up each genome, and the fitness list corresponding to each genome.

Below is a clearer description of how it works, as analyzed by Galapagos, David Rutten15.

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Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionaryprinciples

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Figure 12. Galapagos interface

4.4.1.The Process Figure 13 shows a natural landscape of a particular model. The model contains two variables, two values that are allowed to change and are called genes. So, as gene A changes, the fitness of the entire model increases or decreases. But for each value of A, we can also differentiate the B gene, resulting in better or worse combinations of A and B. Each combination of A and B results in a particular physical state and this

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physical state is expressed as the height of the natural landscape. It's the job of the algorithm to find the highest peak in this landscape.

Figure 13

Of course, many problems are defined not only by two but many genes, so we can no longer talk about "landscape" in the narrow sense. A model with twelve genes would be a twelve-dimensional tumor expressed in thirteen dimensions instead of a two-dimensional volume expressed in three dimensions. Because it is impossible to visualize, only two-dimensional models will be used, but it should be noted that when we talk about "landscape," it can mean something far more complex than the picture above.

As the solver starts, he has no idea about the real shape of the landscape. Indeed, if we knew the shape, we would not have had to deal with it from the beginning. So the solver's initial step is to fill the "landscape" with a random collection of genomes. A genome is nothing more than a specific value for each combination of genes. In the above case, a genome may for example be {A = 0.2 B = 0.5}. The solver will then

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calculate fitness for each of these random genomes, giving us the distribution shown in Figure 14.

Figure 14

Figure 15

Once the solver determines how appropriate each genome is (that is, the elevation of the red dots), it creates a hierarchy from the most capable to the less. We are looking for high ground in the landscape and it is reasonable to assume that the higher genomes are closer to the possible high levels than the low ones. Therefore, o solver focuses on these combinations (figure 15) and repeats the above procedure until it finds the optimal combination of genes.

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4.4.2.Fitness Function In Genetic Algorithms, fitness is an easy concept: it is the suitability, the result that we want to achieve. We are trying to solve a specific problem and therefore we know what we want to achieve. If, for example, we are trying to place an object so that it can be produced with minimal waste material, there is a very strict sense of suitability that leaves no room for arguments.

In figure 16, the green figure is surrounded by two frames. B has a smaller surface than A and is therefore more 'fit' and therefore has a greater fitness.

Figure 16

When we need to print 3D a solid shape, it's a good idea to rotate it until the minimum amount of material to be used in construction is required.

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For a truly minimal box, we need at least three pivot axes, but since this will not allow for an understanding of the natural landscape produced, we will limit the rotation around the two axes X and Y. Thus, A will represent the rotation around X axis and the B gene will represent rotation around the Y axis. No rotation greater than 360 degrees, so both genes have limited boundaries. Figure 17 shows the rotation around an axis.

Figure 17

Figure 18 shows the natural landscape of the problem. The first thing we notice is that the landscape is periodical. That is, it is repeated every 90 degrees in both directions. Also, this landscape is actually inverted as we look for the minimum volume, not the maximum. Thus, the orange peaks actually represent the worst solutions to this problem. When looking at the bottom of this landscape, we take a somewhat different view, as shown in Figure 19.

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Figure 18

4.Software

Figure 19

We can summarize the genome-fitness relationship in a two-dimensional graph, as shown in Figure 20.

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Figure 20

In this case, there are no local maxima or minima and once the algorithm detects the maximum or minimum fitness, it is safe to conclude that this is the optimal one.

However, there are instances where the graph has locally maximum / minimum, as shown in Figure 21.

Figure 21

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In the first case, the peaks are at a great distance and therefore it is easy to lose the second peak by accidentally sampling the landscape. Once a lucky genome finds the top on the left, its descendants will quickly flood the low peak causing the rest of the population to disappear. Now it's even less likely to find the best top on the right. The smaller the solution areas and the greater the distance between them, the more difficult it is to resolve a problem with a genetic algorithm.

In the second case, although there is strictly no local maximum / minimum, there is also no improvement in the "plateaus". A genome located in the middle of one of these horizontal sections does not know where to go. If he makes a step to the left, nothing changes, the same if he makes a step to the right.

In the third case the landscape has a high degree of detail. A landscape can be continuous and yet so detailed that it is impossible to make some smart choices about fitness.

4.4.3. Selection Mechanism Galapagos has several mechanisms for selecting genomes. These are essentially those genomes that each generation is selected by the algorithm to "mate" to produce better results in the next generation.

One mechanism is Isotropic Selection. In this case, all genomes have the same chances at any point in the graph as they appear, as shown in Figure 22. One can think that this mechanism does not advance the process, however, slows the reproduction rate of a population and works

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as a protection tool to prevent locally creating maxima / minima that may not respond to the search.

Figure 22

Another selection mechanism case is the Exclusive Selection (Figure 23).

Here, only the top N% of the population is selected.

Figure 23

Finally, there is Biased Selection (Figure 24), where the probability of mating increases as fitness increases. All genomes have a choice, but those with greater fitness are more likely to play.

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Figure 24

4.4.4. Coupling Algorithm Once the selection process has preceded, another genome must be found to "mate". There are, of course, many ways in which the second genome could be selected, but Galapagos at this time only allows one: selection based on the genomic distance. In order to understand this concept, we first need to present how Genome Map works (figure 25).

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Figure 25

All genomes appear as dots in a grid. The distance between two genomes in the grid is approximately proportional to the distance between the genomes in the gene space. The word is almost used because it is impossible to draw a map with exact distances. A single genome is defined by a number of genes. Therefore, the distance between two genomes is an N-dimensional value, where N equals the number of genes. It is not possible to accurately display a set of Ndimensional points on a two-dimensional screen, so Genome Map is just a coarse approach. Also, the axes of this graph have no meaning, the only information that the Genome Map transfers is what genomes are similar (close) and what genomes are different.

Let us imagine that the genome in the red circle is selected (Figure 26).

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Figure 26

A mechanism for selecting the second genome would be the selection of nearby genomes (figure 27), which could be called incest. In the language of evolutionary algorithms, the greatest risk of incest is the rapid decline in population diversity. Low diversity reduces the chances of finding alternatives and is therefore at risk of sticking to local extremes (maximum / minimum).

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Figure 27

Another option is the closure of nearby genomes (Figure 28), but this mechanism is equally detrimental.

Figure 28

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It seems that the best choice is the balance between the two mechanisms above (Figure 29). Galapagos allows for the determination of the in-breeding factor (between -100% and + 100%) to guide this relative offset.

Figure 29

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4.4.5. Coalescence Algorithm Once a mate has been selected, offspring needs to be generated. The biological process of gene recombination is horrendously complicated and itself subject to evolution (meiotic drive for example). The digital variant is much more basic. This is partially because genes in evolutionary algorithms are not very similar to biological genes. Genes in evolutionary solvers like Galapagos behave like floating point numbers that can assume all the values between two numerical extremes.

When we mate two genomes, we need to decide what values to assign to the genes of the offspring. Again, Galapagos provides several mechanisms for achieving this.

Imagine that we have two genomes of four genes each (Figure 30).

Figure 30

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The combination of M and D is possibly a completely symmetrical process (Figure 31).

Figure 31

A second mechanism is to define new genome values based on both genomes, by averaging the initial values (Figure 32).

Figure 32

It is also possible to define the percentage of the values of the original genes that affect the creation of the offspring. If, for example, gene M has a higher fitness than D, its gene values will be more prominent in offspring (Figure 33).

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Figure 33

4.4.6. Mutation Factories All of these processes tend to reduce diversity in a population. The only mechanism that can introduce diversity is the mutation. There are several types of mutation in the Galapagos core, although the nature of the application at Grasshopper currently limits the possible mutation to Point Mutation alone.

Below is an explanation of Genome Graphs. A popular way to display multidimensional points in a two-dimensional graph is to design them as a series of lines linking different values to a set of vertical bars. Each line represents a single dimension. In this way, we can easily display not only points with any number of dimensions, but also points with a different number of dimensions in the same graph.

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Figure 34

In Figure 34, for example, we have a genome consisting of 5 genes. This genome is therefore a point in a five dimensional space that describes this particular species. When G0 is drawn to â…“, it means that the value is one-third between the minimum and the maximum allowable limit. The benefit of this graph is that it becomes fairly easy to locate the subspecies in a population. When we apply mutations to a genome, we will see a change in the graph, as each genome has a unique graph.

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Figure 35

Figure 35 shows a Point mutation, where a unique value of a gene is changed. This is today the only type of mutation that is possible in Galapagos. We could also exchange two neighboring values, so we will have an Inversion Mutation (Figure 36).

Figure 36

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Inversion mutations are useful only when the genes have a very specific relationship. It tends to drastically modify a genome and so in most cases also drastically modify fitness, a process that is almost always devastating.

Two examples of mutations that cannot be used in a species requiring a fixed number of genes are the Addition Mutation and Deletion Mutation mutations (Figures 37, 38, respectively).

Figure 37

Figure 38

5.Examples

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5.Examples A brief description of some research by designers who have used Genetic Algorithms to find an optimal solution to design problems related to energy issues is made.

The design of a student housing complex on the campus of the University of Patras has served as a starting point for experimenting with Chronis, Liapis, Sybethero16 with a proposed design methodology that emphasizes bioclimatic principles and uses new computational tools and processes. The software used to develop the model parameters and design algorithms connecting the building to the climatic characteristics of the space is Bentley's Generative Components (Figures 39, 40).

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Chronis A., Liapi K., Sibetheros I. (2012) A parametric approach to the bioclimatic design of large scale projects: The case of a student housing complex, Automation in Construction 22, 24–35

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Figure 39

5.Examples

5.Examples

Figure 40

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5.Examples

An experiment by Shi and Yang17 examines the percentage of transparent surfaces on each face. The design problem is a rectangular building with given dimensions 4.2 × 4.2 × 3.0 m, located in Nanjing, China. The building has a window on each wall with a fixed total window surface. As, Ae, Aw, An denote the window region in the south, east, west and north sides respectively. The total window area is 7.29 m2. The following equations are used: As = s × 7.29 Ae = e × (7.29-As) Aw = w × (7.29-As-Ae) An = 7.29-As-Ae-Aw where s, e, w are numbers between 0.01 and 0.99 In this case, the optimal solution, shown in Figure 41, has the minimum total energy consumption. The software used was Galapagos.

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Shi Χ., Yang W. (2013) Performance-driven architectural design and optimization technique from a perspective of architects, Automation in Construction 32, 125–135

5.Examples

Figure 41

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5.Examples

A survey by Portugal and Guedes18, also used Galapagos to determine the optimal size of a given geometry (window) in order to reduce energy consumption (Figure 42). A dot matrix is formed on the surface selected to apply the window (s). The shape of the window is recognized by combining four points on the surface. The shape of the window is defined by only two control points, which automatically create two secondary points. While the control points move, the secondary ones move accordingly (b). The window is then defined by subtracting it from the surface of the wall. In addition, the thickness of the wall is defined as a flexible parameter (c).

Figure 42

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Portugal, V. & Guedes, M. C. (2012) Informed Parameterization: Optimization of building openings generation. LIMA, 28th International PLEA Conference

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Another research by Ercan and Elias-Ozkan19 studies the optimization of the shading system (Figure 43). The original shading design had a fixed angle across the face (a). To break this monotony, the possibility of changing the orientation of the shadow elements along the height of the face (b) was explored. This design effort aims to achieve an aesthetically dynamic form of shading system, while optimizing the quality of lighting in office spaces. The software used was Galapagos.

Figure 43

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Ercan B., Elias-Ozkan S.T. (2015) Performance-based parametric design explorations: A method for generating appropriate building components, Elsevier Ltd

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An experiment by Konis, Gamas, Kensek20 demonstrates how it is possible for the genetic algorithm to define essentially geometric parameters of the shell in the initial design phase. A study is being carried out on a building site in different locations and the results proposed are based on parameters of the basic geometry of the floor plan, percentage and location of the openings and shading systems, as the worst-case solution is also presented. This example illustrates the differentiation of the solutions according to the weather data of the area (Figures 44, 45).

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Konis K., Gamas A., Kensek K. (2016) Passive performance and building form: An optimization framework for early-stage design support, Solar Energy 125, 161–179

5.Examples

Figure 44

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Figure 45

5.Examples

5.Examples

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Chalabee's21 experiment is investigating the most efficient percentage of transparent surface in the southern face of an office space in six different cases, with the main criterion being maximum natural lighting.

The study is conducted for two different box-shaped boxes with the same floor area (20 × 20) and (40 × 10). The height of the building is a flexible element (3m, 6m and 9m) showing the effect of building height on energy consumption. The Galapagos software was once again used (Figure 46).

Figure 46

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Chalabee Η. (2013) Performance-based architectural design: Optimisation of building opening generation using generative algorithms, University of Sheffield

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6.Case Study

6.Case Study 6.1.Given Data The study concerns an exercise, defining rules that form simple geometries and draw out basic conclusions about the shell parameters that greatly influence energy demand and consumption. Obviously, no more complex geometries were chosen for two main reasons: •The architectural and design capabilities of each architect are innumerable and depend on various parameters that are relevant to the client's requirements, location, legislation, etc. As the present research is a study not limited by external requirements, the simplification of the design parameters was considered the best technique. •The results of the survey provide a great deal of information to help us draw conclusions about the impact of shell morphology on energy demand, which would not be the case if we set up more complex geometries.

The study examines whether the genetic algorithm can determine basic parameters of geometry such as dimensions, orientation and percentage of transparent surfaces and their position, thickness of insulation to determine the U-value coefficient of thermal conductivity, dimensions of shades to the minimum energy demand.

A variety of climatic data was selected, different sites, in order to better understand the results and examine the functioning of the genetic

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algorithm, as well as to draw conclusions about the geometry of the shell and how it impacts on its energy demand and consumption building.

The experiment chosen concerns a single-family house with a total area of 100m2 with a four-story roof.

Data that is kept constant is shown in the tables below22.

Table 1 General characteristics

Total building area Number of floors Floor height Roof tilt Number of energy zones Number of heating zones

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100m2 1 3m 30° 2 1

The values of Table 2 were determined based on Tables 2.1, 2.2, 2.3 of the Technical Instruction of the Technical Chamber of Greece, TITCG 20701-1 / 2017, Edition A, the values of Table 3, Table 4, Table 5 were determined on the basis of Table 1 of the Technical Instruction of the Technical Chamber of Greece, TITCG 20701-2 / 2017, Edition A and the values in Table 6 were determined on the basis of Tables 9 and 10 of the Technical Instructions of the Technical Chamber of Greece, TITCG 20701-2 / 2017, Edition A.

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6.Case Study

Table 2 Internal operating conditions

Operation hours Operation days Operation months Heating period Cooling period Average internal heating temperature Average internal cooling temperature Average indoor relative humidity in the winter Average indoor relative humidity in the summer Fresh air infiltration Number of people per area Equipment load Lighting load

18h 7 12 From 15/10 to 30/04 From 1/06 to 31/08 20 26 40 45 0.003m3/s-m2 0.03ppl/m2 2Watt/m2 3.2Watt/m2

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Table 3 Wall construction

Material

Thickness (m)

Conductivity (W/m-K)

Density (kg/m3)

Specific heat (J/kg-K)

Lime plaster (1.4.2.)

Brick (1.7.2.1.)

Expanded polystyrene (7.3.3.2.)

Brick (1.7.2.1.)

Lime plaster (1.4.2.)

0.02

0.87

1800

1000

0.09

0.49

1200

1000

float23

0.03

35

1450

0.09

0.49

1200

1000

0.02

0.87

1800

1000

23

Parameter that is varied by Galapagos

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6.Case Study

Table 4 Floor construction

Material

Thickness (m)

Conductivity (W/m-K)

Density (kg/m3)

Specific heat (J/kg-K)

Lime plaster (1.4.2.)

Expanded polystyrene (7.3.3.2.)

0.02

0.87

1800

1000

0.06

0.03

35

1450

Reinforced concrete (1.5.3.)

0.15

2.3

2300

1000

Plain concrete (1.5.5.)

Cement mortar (1.4.3.)

Tiles (4.7.2.)

0.08

0.2

500

1000

0.02

1.4

2000

1100

0.005

1.84

2000

840

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Table 5 Roof construction

Material

Thickness (m)

Conductivity (W/m-K)

Density (kg/m3)

Specific heat (J/kg-K)

Lime plaster (1.4.2.)

Expanded polystyrene (7.3.3.2.)

0.02

0.87

1800

1000

0.06

0.03

35

1450

Reinforced concrete (1.5.3.)

0.15

2.3

2300

1000

Plain concrete (1.5.5.)

Cement mortar (1.4.3.)

Tiles (4.7.2.)

0.08

0.2

500

1000

0.02

1.4

2000

1100

0.005

1.84

2000

840

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6.Case Study

Table 6 Window construction

Frame thickness Dimensions Space material U-value for frame U-value for glazing Solar heat gain coefficient (SHGC) Visible transmittance (VT)

7cm 4-8-4 Argon 2.5 W/(m2K) 1.9 W/(m2K) 0.7 0.8

6.2.Methodology There are several methods the designer / operator of the program can use to produce forms. One of these is to use the genetic algorithm by switching values to one parameter at a time, keeping the rest of the parameters constant. This method is effective especially when there are limitations, for example when the orientation of the building is given and we are looking at optimizing geometry by changing other values. This of course does not produce the optimal result, but the optimal choice with this. The methodology chosen is to find a solution by alternating all the parameters in parallel. In this way we find the most efficient solution with the optimum combination of parameters that gives us less energy consumption.

Initially, the basic geometry of the building, the roof, the openings and the shades were designed. Then the construction, with the definition of

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63

the materials and hence the overall heat transfer coefficient and the determination of the loads of the energy zones. Finally, climatic data and orientation were defined.

The experiment was repeated in two stages: 1. changing weather data (two different scenarios with climatic data from Athens and Thessaloniki) 2. varying the percentage of transparent surfaces (two different scenarios with a percentage of transparent surfaces of 10% and 20%); Overall, the experiments carried out were four.

The basic geometry of the building is a rectangular ground plan, a total area of 100m2 that is kept constant by simply changing the proportion of the sides making the profile square or rectangular. The roof is a 30 degree tilt. Transparent surfaces have a given area of 10m2 or 20m2 (10% and 20% on the floor respectively) and the genetic algorithm selects their percentage of distribution on each side while calculating the dimensions of the beads. As far as construction is concerned, the only value that changes is that of the thickness of the insulation.

Table 7 summarizes the parameters and limits of their values.

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6.Case Study

Table 7

Parameter Dimensions x y Orientation Transparent surfaces Percentage of north face Percentage of east face Percentage of south face Percentage of west face Wall insulation thickness Cantilever Length Width

Minimum value

Maximum value

5m 5m 0°

10m 10m 90°

0%

95%

0%

95%

0%

95%

0%

95%

0.05m

0.10m

0m 0m

2.2m 1m

6.3.Results The results of the Genetic Algorithm for each of the experiments are analyzed below.

6.Case Study

65

6.3.1.Experiment 1: Athens, percentage of transparent surfaces: 10% of the floor

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6.Case Study

6.3.2.Experiment 2: Thessaloniki, percentage of transparent surfaces: 10% of the floor

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67

6.3.3.Experiment 3: Athens, percentage of transparent surfaces: 20% of the floor

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6.Case Study

6.3.4.Experiment 4: Thessaloniki, percentage of transparent surfaces: 20% of the floor

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69

The results of the survey are shown in Table 8.

Table 8

Dimensions x y Orientation Transparent surfaces Percentage of north face Percentage of east face Percentage of south face Percentage of west face Insulation thickness Cantilever Length Width Heating energy Cooling energy Total thermal load

Athens 10%

Thessaloniki 10%

Athens 20%

Thessaloniki 20%

12.5m 8m

12.5m 8m

12.5m 8m

12.5m 8m

83째

86.1째

80.2째

75째

0%

0%

0%

0%

0%

0%

0%

0%

26.6%

26.7%

53.3%

53.3%

0%

0%

0%

0%

10cm

10cm

10cm

10cm

0.9m 0.9m 2.52m-0.5m 2.92m-0.7m

1.7m 1.5m 3.43m-0.2m 4.03m-0.5m

806.99 KWh 3549.39 KWh 4356.38 KWh

767.68 KWh 3914.17 KWh 4681.85 KWh

1768.31 KWh 2704.62 KWh 4472.93 KWh

1638.70 KWh 3013.18 KWh 4651.88 KWh

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7.Conclusions

7.Conclusions 7.1.Disadvantages In terms of parametric design taking energy factors into housing, a number of problems are encountered that the architect-user of the software is required to address during design.

•Needed time One of the biggest disadvantages of using a genetic algorithm is the length of data processing. The genetic algorithm (Galapagos) in every effort, altering the parameters chosen by the operator, forces the Ladybug and Honeybee plugins to resume the energy calculation. This process alone is not time-consuming, however, when the algorithm looks for a solution to thousands of combinations of parameters, the duration can overcome reasonable time data, even for a simple problem.

Obviously, the time required depends on the number of combinations the algorithm has to process. This is also the point where the architect is called upon to cope, minimizing as possible possible combinations, making smart use of the software tools provided.

•Programming knowledge Although the available software tools offer excellent design capabilities in terms of morphology, they often appear inadequate when

7.Conclusions

71

Galapagos is to be used. The algorithm, while being an intelligent tool, does not have inbound parameter settings. The only option required is to maximize or minimize a single value. This presents two problems: 1. Parameters must be set before entering the algorithm if the user wants specific solutions. For example, if we have ten different sliders that accept values from 0 to 100 and we do not want to have the same value on any of the sliders, this setting must be done with Grasshopper tools and not with Galapagos. The difficulty lies in the fact that either complex and numerous tools will be used to "load" the file and force the algorithm to search for more combinations, either using a programming language, knowledge that architects usually do not have and have difficulty acquainted with With these. It is therefore possible to help and involve a developer or someone else.

2. The value that Galapagos accepts and is sought to maximize or minimize it is unique. Therefore, in this case, appropriate actions are required before defining this size that respond to the search, as we can not maximize a value and minimize another. •Unacceptable results Since the use of the genetic algorithm is for the purpose of producing forms and shells intended for human use, it is necessary to take into account criteria that are not only related to energy factors. The results generated by the genetic algorithm, while fully responsive to search, may not be aesthetically acceptable to users. As there is no way of defining "nice", the architect is called upon to make design decisions.

Consequently, the results of using the genetic algorithm may not be appropriate in any case.

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7.Conclusions

7.2.Advantages •Familiar graphical user interface The Grasshopper plugin is designed in a way that meets the user's needs.

The interface consists of components that correspond to specific functions, accept inputs and produce outputs. The components are connected to each other via cables that always connect the outputs to the inputs. The generated form is displayed in real time in the Rhino window, enabling the user to have direct contact with the model, altering at any time.

Therefore, the use of both Grasshopper and Galapagos seems to be an easy process, and the familiarization of the user with them can be done easily and quickly. •Operational efficiency The results of the genetic algorithm are based on measurable sizes and are therefore at least functional. Architects often design shells using systems that promise proper energy management, but most of the time the sizes are not measurable at the initial design stage, the final design may not be effective and sustainable. As the solutions produced by the Genetic Algorithm are based on actual user-defined values, efficiency is a given. •Flexibility The tools provided by Grasshopper are numerous and have various types of information, giving the user the opportunity to produce forms

7.Conclusions

73

whose result will be the same regardless of how they were created. At the same time, it is also easier to familiarize the user with the program and speed up learning. •Internet community The original goal of Grasshopper's tool makers was to initially facilitate the architectural process for designers involved in bioclimatic tactics.

Apart from intelligent design and readily available guidance and advice, the internet provides numerous forums that serve each user and provide solutions to questions that may arise in no time, while the unlimited online resources by users around the world facilitate a large degree process. •Open source application Grasshopper's tools are open source applications, enabling developers to create new tools, easily integrated into Grasshopper's graphical environment. So there is growing platform development and continuous improvement of tools. At the same time, there is free software availability, making it one of the most useful tools that can be used by everyone to solve simple but complex problems that concern all areas of modern architecture and design.

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8.Epilogue

8.Epilogue At the initial stage of design, the environmental simulations of the building have an important role in the architectural design process to determine the best performance of the building. To involve these simulations requires an advanced simulation software package.

However, the simulation process is based on scenarios, generating the need to develop a computational tool that is capable of automatically generating design alternatives. As a result, the solution uses the Genetic Algorithm approach as a computational method for the production of alternative architectural designs.

The use of these tools provides a good guidance for architects in the design process. They are also able to optimize different building blocks by changing only a few parts of the definition. However, these tools are still relatively new and have limitations. One of the main limitations is that it is a time consuming process and the performance of the process is based on the performance of the used computers.

At the end of this research, we draw conclusions on the use of genetic algorithms in building shells for lower energy yields.

This exercise produces valuable results and helps us to better understand basic principles of bioclimatic architecture. The genetic algorithm is a useful tool for exploring design boundaries and can create more efficient shells.

However, in practice, it is unlikely to find the ideal conditions under which the experiments were conducted. In cases where a solution to more

8.Epilogue

75

complex problems is sought, the definition of parameters as well as the computational power provided can be a barrier to the effective use of such tools.

Even if we envision an ideal environment in the future that will provide all these possibilities to architects, the results of such searches would be rather disappointing, at least as far as the aesthetic aspect is concerned.

As far as bioclimatic issues are concerned, the requirements of a functional shell are already known, even the results of this research work were not surprising. The architect's role is to be flexible and able to use the computing tools to turn them into an ally in the effort to solve problems, without ignoring the sensory factors that make the work satisfactory.

Based on this principle, we can safely say that the Genetic Algorithm, namely Galapagos, is a powerful tool in designing smaller-scale bioclimatic or other systems. The architect, based on a specific plan and goals for his design, can rely on genetic algorithms for design segments, limiting the parameters and thus satisfying both architectural process, operational efficiency and aesthetic satisfaction.

The design platform through which works, Rhinoceros 3D, is a widely popular program among architects making the Genetic Algorithm a practice that can be easily adopted by designers.

Undoubtedly, the future of Architecture can be based on the help of similar computing tools, as rapidly evolving technological developments broaden the boundaries of architectural design.

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8.Epilogue

Design based on parametric and genetic factors is an emerging trend in architecture over the last decades and is considered a valuable tool for exploring potential design and enriching the architectural synthesis process. In designing forms or systems, this method provides dynamic geometry and data control, allowing the designer to look for suitable solutions for complex problems using multiple variations. Design tools, such as Grasshopper for Rhinoceros 3D and Galapagos, offer the opportunity to implement parametric design concepts to automate complex tasks.

9.Figures

77

9.Figures Figure 1 Fasoulaki E. (2007) Genetic Algorithms in Architecture: a Necessity or a Trend?, Department of Architecture, Massachusetts Institute of Technology Figure 2 Jabi W. (2013) Parametric Design for Architecture. London, Laurence King Publishing Ltd Figure 3 Grasshopper 3D, https://en.wikipedia.org/wiki/Grasshopper_3D Figure 4 Grasshopper 3D, https://en.wikipedia.org/wiki/Grasshopper_3D Figure 5 Ladybug Tools, https://www.grasshopper3d.com/group/ladybug Figure 6 Ladybug Tools, https://www.grasshopper3d.com/group/ladybug Figure 7 Vijini Mallawaarachchi, Introduction to Genetic Algorithms Including Example Code, https://towardsdatascience.com/introduction-to-genetic-algorithmsincluding-example-code-e396e98d8bf3 Figure 8 Vijini Mallawaarachchi, Introduction to Genetic Algorithms Including Example Code, https://towardsdatascience.com/introduction-to-genetic-algorithmsincluding-example-code-e396e98d8bf3 Figure 9 Vijini Mallawaarachchi, Introduction to Genetic Algorithms Including Example Code,

78

9.Figures

https://towardsdatascience.com/introduction-to-genetic-algorithmsincluding-example-code-e396e98d8bf3 Figure 10 Vijini Mallawaarachchi, Introduction to Genetic Algorithms Including Example Code, https://towardsdatascience.com/introduction-to-genetic-algorithmsincluding-example-code-e396e98d8bf3 Figure 11 Vijini Mallawaarachchi, Introduction to Genetic Algorithms Including Example Code, https://towardsdatascience.com/introduction-to-genetic-algorithmsincluding-example-code-e396e98d8bf3 Figure 12 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 13Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 14 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 15 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 16 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 17 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles

9.Figures

79

Figure 18 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 19 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 20 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 21 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 22 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 23 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 24 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 25 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 26 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 27 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 28 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles

80

9.Figures

Figure 29 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 30 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 31 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 32 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 33 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 34 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 35 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 36 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 37 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 38 Evolutionary Principles, Galapagos, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles Figure 39 Chronis A., Liapi K., Sibetheros I. (2012) A parametric approach to the bioclimatic design of large scale projects: The case of a student housing complex, Automation in Construction 22, 24–35

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81

Figure 40 Chronis A., Liapi K., Sibetheros I. (2012) A parametric approach to the bioclimatic design of large scale projects: The case of a student housing complex, Automation in Construction 22, 24–35 Figure 41 Shi Χ., Yang W. (2013) Performance-driven architectural design and optimization technique from a perspective of architects, Automation in Construction 32, 125–135 Figure 42 Portugal, V. & Guedes, M. C. (2012) Informed Parameterization: Optimization of building openings generation. LIMA, 28th International PLEA Conference Figure 43 Ercan B., Elias-Ozkan S.T. (2015) Performance-based parametric design explorations: A method for generating appropriate building components, Elsevier Ltd Figure 44 Konis K., Gamas A., Kensek K. (2016) Passive performance and building form: An optimization framework for early-stage design support, Solar Energy 125, 161–179 Figure 45 Konis K., Gamas A., Kensek K. (2016) Passive performance and building form: An optimization framework for early-stage design support, Solar Energy 125, 161–179 Figure 46 Chalabee Η. (2013) Performance-based architectural design: Optimisation of building opening generation using generative algorithms, University of Sheffield

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10.Bibliography Chalabee Η. (2013) Performance-based architectural design: Optimisation of building opening generation using generative algorithms, University of Sheffield Chong, Y. T. (2009). A heuristic-based approach to conceptual design, Research in Engineering Design, 20(2), 97-116 Chronis A., Liapi K., Sibetheros I. (2012) A parametric approach to the bioclimatic design of large scale projects: The case of a student housing complex, Automation in Construction 22, 24–35 Ercan B., Elias-Ozkan S.T. (2015) Performance-based parametric design explorations: A method for generating appropriate building components, Elsevier Ltd Fasoulaki E. (2007) Genetic Algorithms in Architecture: a Necessity or a Trend?, Department of Architecture, Massachusetts Institute of Technology Guillemin, A., Morel, N. (2001) An innovative lighting controller integrated in a self-adaptive building control system, Energy and Buildings, 33(5), 477-487 Hemsatha T., Bandhosseinib K.A. (2015) Building Design with Energy Performance as Primary Agent, Energy Procedia 78, 3049 – 3054 Jabi W. (2013) Parametric Design for Architecture, London, Laurence King Publishing Ltd

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