Vernacular symbiosis

Vernacular

Symbiosis An evolutionary climate-responsive neighbourhood model

Shanky Jain(Msc), Fatemeh Nasseri(MArch),Yasaman Mousavi(MArch)

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Acknowledgements We would like to thank, Mike Weinstock for his guidance and support throughout the entire year. His experience and assistance helped us achieve progress and knowledge. His commitment and support enabled us to constantly explore new directions in the design and allowed us to take maximum advantage of the course.

Toni Kotnik, our studio master. His knowledge and assistance, especially, his help with mathematical problems enabled us to push the boundaries of the project.

George Jeronimides for professional technical guidance and advices helping through the whole design process.

All EmTech teaching staff, as well as all guest lecturers and consultants; the knowledge and experience we gained during the first phase of the course provided us with the tools and skills necessary to progress on this project.

Pierluigi D’Acunto and Jeroen Janssen, our friends, who always assisted us for problems related to computation.

Finally, we would like thank our families, friends and peers for constant moral support and encouragement.

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Abstract This research explores new computational analysis and design techniques in order to develop a bio-climatic strategy for urban development at the scale of the neighbourhood. A model is developed that incorporates micro-climatic physics that is tested on selected vernacular morphologies. The abstracted principles drive the design of new morphologies (generated by genetic algorithm and environmental softwares) suitable for current patterns of living, responsive to new demographics, social structures and materials within the context of contemporary cities in arid climates.

Contents Chapter 1 - Introduction.......................................................................................................................................11-19 1.1 Urbanization and population growth 1.2 Climate change 1.3 Middle east and current trends of design 1.4 Conclusions

Chapter 2 - Methods...............................................................................................................................................21-33 2.1.Computational city design 2.2.Computational methodologies A.Analytical methodologies B.Design methodologies C.Tools

Chapter 3 - Analysis of vernacular city morphologies ..................................................................35-57

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3.1 Introduction 3.2 Learning from evolved settlements (hot-arid regions of Middle east) A.Case study 1 - Shibam,Yemen B.Case study 2 - Yazd,Iran 3.3 Comparative analysis 3.4 Conclusions

Chapter 4 - First experiments for generations of urban patches...................................59-91 4.1. Introduction 4.2. Genetic process A.Block generation B.Patch generation 4.3. Conclusions

Chapter 5 - Design development.........................................................................................93-125 5.1. Introduction 5.2. Experimental site 5.3. Design A.Seed generation B.Distribution of public spaces C.Aggregation of patches 5.4.Conclusions 5.5.Challenges and further developments

Bibliography .................................................................................................................126-127 Appendix ......................................................................................................................129-157

introduction 9

Chapter 1 - Introduction................................................................................................................................................11-19 1.1 Urbanization and population growth 1.2 Climate change 1.3 Middle East and current trends of design 1.4 Conclusions

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World population density(km2) 0

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1.1 Urbanization and population The wave of urban growth the world is experiencing currently can be rightly called the largest one in the history of mankind. Through most of the history humans have lived a rural life dependent on agriculture for survival. Statistics reflect that in 1800, only 3 percent of the world’s population lived in urban areas. This number grew to 14 percent by 1900 and 30 percent by 1950. In 2008; this number was equally distributed between rural and urban areas, with half of world’s population living in towns and cities. The urban population is expected to reach 70 percent of the world population by 2050.

As per revised statistics in 2010, the world population is expected to touch 10 billion by 2100. The population growth would be intensive in high-fertility countries of sub-Saharan Africa, Gulf where the figures are expected to triple the existing inhabitants. Whereas during the same time, the population of intermediate fertility countries like India, United States and most of Latin American countries, will increase by just 26 per cent, while that of Europe, China and Australia, will reduce by about 20 per cent.

Thus most of the growth would primarily be concentrated in developing countries of Middle East and Asia. Also this new growth would occur in smaller towns and cities which are yet to capture public attention unlike other mega cities. The urban transformation intended to literally change the face of these countries would be driven primarily by the forces which will engage them to compete on a global scale. Also these new places have fewer resources to respond to such a magnitude of change. Thus it becomes important to utilize the available resources efficiently and apply them for the most apt purpose. Consequently, it is necessary to adopt strategies that would guide the urban growth in a much holistic way and with a sustainable approach.

Fig 1.1 World Population density map [Online] Available at - http://en.wikipedia. org/wiki/File:Population_density_with_key [Accessed August 15, 2011].

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The Middle East has experienced a dramatic rise in population as compared to other parts of the developing countries in the world since 1970’s. In the ten years between 1976 and 1986, the population of Iran grew by 50%. However, the average expected growth rate of region is 2.1% by 2025. The figures in some of the countries specially the gulf countries like U.A.E, Kuwait,Qatar are highest in the world reaching up to 4-5%. Gulf countries thus in particular can be considered to be leading the race to this metropolitan future. However these regions face an urban concern as rural inhabitants migrate to the urban areas in search of work and generally improved conditions. This has led to a disproportionate rise in the urban population called the urban drift which has been accompanied by population growth. A huge strain is put on the already struggling urban infrastructure, water availability, employment. The new Middle east would inevitably be a veritable mix of cultures, languages, food and most importantly identities. And it is this potent mixture of rapid urbanization and globalization that points toward potential sociocultural challenges arising with the surging tide of urbanization.

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Fig 1.2 Estimated World Population growth rate[Online] Available at http://1.bp.blogspot.com/-q7HdCdlZadU

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Increasein in mean mean Increase temperature(°C) (oC) temperature 2 - 3 3 - 4 4 - 5

Fig 1.3 Estimated rise in temperature by 2050 [Online] Available at -http://en.wikipedia.org/wiki/ Climate change [Accessed August 15, 2011]

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The Middle East region covers 10 million square kilo meters. The region The model projections for temperature change over 2030, 2070 and has wide variations in relation to climatic conditions, with 90% of its land 2100 indicate a steady rise of temperature in most of the region. Modclassified as arid and dry sub humid. Average annual temperatures, as els are projecting hotter, drier and less predictable climate, resulting well as maximum and minimum temperatures, also vary from freezing in a drop in water run off by 20-30% in most of the region by 2050. to over 50 degrees Celsius (ºC), depending on the season and location.

The temperatures are predicted to rise between 3 to 5 degrees CelClimate change presents a real threat of severe environmental, economic sius, which is already too high for human comfort. On the other hand and political impacts in this region. The region is already vulnerable to UAE has the highest carbon footprint on a per capita basis in the world. many non-climate stresses. Climate changes and after effects are likely to Four countries in the gulf i.e. Qatar, UAE, Bahrain and Kuwait are the Sources: IPCC 3rd assessment Synthesis Report, 2001 intensify this vulnerability, leading to large scale instability. biggest offenders. The people of the Middle East are accustomed to The regions emissions of greenhouse gases (GHG) are generally small coping with a warm and arid climate. However, the projected changes (less than 5% of the world’s total),and in per capita terms. The oil proin temperature and precipitation may be beyond the level of human ducing countries contribute the most to the amount of emissions which comfort. Therefore, selection of appropriate adaptation strategies will is growing. Currently this emission rate is the third largest in the world be critical. New methods and techniques of tackling this problem efaccording to the 2005 readings and more than 3 times faster than the fectively will be required. world’s average, most of it coming from fuel combustion .

Increase in mean temperature (째C) 2 - 3 3 - 4 4 - 5 5 - 6

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Fig 1.4 Estimated change in mean precipitation Report,[Online] 2001Available at -http://en.wikipedia.org/wiki/ Precipitation [Accessed August 15, 2011]

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This crisis of Global warming seen in many parts of the world is a complex problem to address. It is necessary to reduce the worldwide carbon emissions in next few decades. This would mean we must rely less on fossil fuels and other nonrenewable sources for energy.

In the developing gulf countries, most fossil fuels are utilized by industrial, transportation and building sectors. With the current desires of economic growth it seems difficult to reduce the use of fossil fuels in industrial and transportation sectors. The building industry is the major source of demand for energy and materials that produce greenhouse gases. Yet it can be stated that it still holds the chance to reduce its overall impact in an attempt to reverse the pattern of global warming. Cities that are planned today should therefore respond to such issues and thus they need to adopt strategies that can save the people and help them survive the effects of climate change.

Fig 1.5 World carbon emissions(top), Green house gas emissions (bottom) in 2009 [Online] Available at http://edgar.jrc.ec.europa.eu/2009 [Accessed September 22, 2011]

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1.3 Middle east and current trends of design The Middle East is a current frontline of rampant modernization.

The gulf and its initial development triggered by the discovery of oil are undergoing hyper development to get ready for the eventual depletion of its oil deposit. To sustain the needs of the population growth, the region has seen a rapid growth in its residential developments. In a haste to provide homes, imported western design models are being applied. As a result there is a deviation from cultural roots and local environmental adaptability. Currently, the trend of building high rise buildings is sweeping all cities in the Middle East. They are all trying to build the tallest and the biggest structures to create a global impact as a possible strategy to attract business from around the world with an intention to sustain once the oil reservoirs get over. This trend, started by Dubai has attracted other cities of the gulf. Countries like Kuwait, Saudi Arabia, and Bahrain are rising vertically as a sign of modernity.

Le Corbusier’s model of centralized densities with high rise towers and large open public spaces are considered the optimum solutions for accommodating the densities.

However these building types are new to the context and can be seen as any other building from any other part of the world. With the speed of growth, no careful studies are done to see the adverse impacts of such new prototypes. Also, the same is with the urban design practiced here. As a result the cities are no longer generated by plan, and have become patchworks of developer ‘increments’. Instead of intensification, the cities are conceived to soothe and relax. Infrastructure is no longer conceptual anticipation and more like an afterthought. It represents a collapse of whole design policy. The growing number of tall buildings under construction in Middle Eastern countries is alarming. Their impact on the human, natural, and built environment is not carefully assessed. The sustainability of tall buildings and mega-projects should be guaranteed in order to avoid creating degraded and congested urban environments.

Organization, originality and efficiency should be considered imperatives. But both the urbanism and architecture of the gulf have clearly proven unsustainable.

15 Fig 1.6. aerial view of a typical neighbourhood model in dubai.

[Online] Available at- http:// httpwww.flickr.com/ photosd5e2304382857[ Accessed August 25, 2011]

Fig 1.7. Le Corbusier’s revolution of 20th century from traditional urban fabric to a modern landscape .Most of the cities in the Middle east have adopted this model of progress.

[Book] Cities design and evolution , Stephen Marshall,pg no [3]

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1.4 Conclusions The modern planned urban environments of the Middle Eastern cities are perceived to be inefficient in relation to their responsiveness to the local social structure and environment. The last few decades have seen the most dramatic changes in urban design history, with traditional urban fabric being replaced by a modern urban order. Where once there were terraces, squares, and courtyards are now high rise towers and large spaces for infrastructure and public spaces. The traditional urban forms enable people with things like pedestrian friendly streets, sitting in pavements, cafes in public plazas or courtyards contributing to the Public realm. It is rightly said “Cites are the ultimate human-made habitats, and yet-among all speciesit is perhaps only humans who create habitats that are not fit to live in� [1]

Thus the aim of the study would be to arrive at a better understanding of the vernacular morphologies, their design and evolution. It would aim to comprehend the interrelations between purposive interventions and the resulting urban patterns and products. The study would seek to understand the spatial structure and character of the tissues by examining the pattern of physical forms like streets, buildings and the evolution of mitigation strategies to sustain its inhabitants in the extreme weather.

This understanding can help crystallize possibilities of different alternatives for designing and planning of cities in future “that address the prevailing sociocultural and climatic conditions. Also develop strategies to achieve functional urbanism between the extremes of organic cities and rigidly planned geometrical order.

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computation 19

Chapter 2 - Methods.......................................................................................................................................................21-23 2.1. Computational city design 2.2. Computational methodologies A. Analytical methodologies B. Design methodologies C. Tools

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2.1 Computational city design Since the last few years, the centralized nature of city planning in the major cities of Middle East has created a wide spread reaction against these methods of city planning . This is because our ever-growing desires to make more efficient and effective cities has exceeded in pace with the understanding of the cities and the context for which they need to adapt. These strategies that have changed the face of urban planning are based on limited understanding of the cities, and sold in the name of progress and a better life. Thus there is a need for planning strategies that are more decentralized and unconscious .This approach to the cities is important since it changes our attitude towards design. An approach where planning and design emerge from bottom up, where planning and design is decentralized and in tune with the way city grow and change.

Computational design has been sufficiently powerful to deal with large number of units by simulating bottom up processes like disaggregation space, time and the typology of activities. Thus it has become common to understand the complex patterns of urban morphologies through simulations and representations using new programming languages.

However, these techniques are generally only restricted to urban analysis and fail to provide urban planning solutions. The reason being the logic of computation , that imply quantitative measures whereas the sociocultural aspects remain untouched.

The research thus would explore some of the most acknowledged computational methods that are currently only utilized in urban analysis and explore its potential in urban planning. Also emphasis would be to develop evaluation strategies using environmental and microclimate analysis softwares.

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Fig 2.1 Substrate – ‘Lines likes crystals grow on a computational substrate. A simple perpendicular growth rule creates intricate city-like structures.”

[Online] Available at http://www.complexification. net/gallery/machines/substrate [Accessed July 18, 2011]

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2.2 Computational methodologies The quality of life in Middle Eastern cities can be improved if the factors that tremendously affect the urban micro-climate, are understood and the form of built environment responds to them in an appropriate way. While the current sustainable design methods focus on indoor climate to satisfy the need of occupants, this research is concerned to develop new methods to design climate responsive urban areas and its effect on microclimate.

The aim is to understand the principles of urban planning like building organization and factors like distribution of public, religious and commercial spaces, relationship between streets and building heights, time of travel from built forms to places of interest,public spaces, pedestrian comfort and energy performance of the buildings which would give a fare idea about the adaptability and environmental sustainability of the city tissues.

A series of genetic and generative computational algorithms are explored in order to analyze , generate and optimise urban forms based on specific morphological indicators. These concepts would be analyzed in the research where they would be tested on some existing vernacular city fabrics and the resultant measurements would be evaluated and compared to understand the responsiveness of every fabric to its urban microclimate.

The tests would involve a series of environmental and physical paramaters both for analysis and design that are mentioned in the table.

Urban patches of similar densities from the selected vernacular city tissues would be considered and the patches would then be divided into 3 scales for investigation i.e a building scale, cluster scale and neighbourhood scale. Depending on the scale, various factors which would affect its shaping are considered and analysed computationally.

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Analytical methodologies

Design methodologies

A.Microclimatic factors

C.Patch generation

Primary Factors related to environmental adaptability of the built forms and the spaces between the buildings are analyzed to evaluate the efficiency of the models under consideration. The evaluations are basically numerical values that are extracted from environmental softwares.

With the aim of generating climate responsive urban morphologies , the design process will explore a bottom-up evolutionary system using genetic algorithms to achieve generations of patches (clusters of buildings) which would then be evaluated by environmental parameters to select a few optimized ones.

B.Socio-cultural factors

D.Aggregation and distribution

The microclimate analysis gives a fare idea about the adaptation strategies evolved overtime. However it fails to analyse the socio-cultural and behavioural patterns . Thus research would also aim at understanding the impact of the behavioural patterns on the structuring of morphologies. This study would define various relationship between the prevailing patterns and the evolved spaces like streets , open spaces etc. Generative algorithms would be used to create such associative models.

This phase explore various aggrgation logics that would connect the patches based on some rule based computational systems like cellular automata and generative algorithms.These logics are used to address issues of network and programme distribution at the scale of a neighbourhood.

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Micro-climate factors

Solar radiation

Building elements

Shadow density

Sky view factor

Facade Roof

Pedestrain level

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Fig 2.2 Microclimate simulation of a Urban patch in London.

[Online] Available at http://www.iesd. dmu.ac.uk/ [Accessed September 14, 2011]

A. Microclimate analysis Street comfort and surface exposure are one of the most prominent issues in hot urban regions which often regulate the microclimate. The lack of street shading and self-shading between the buildings often result in increasing the thermal stress for inhabitants .

The most effective shading elements are often vertical objects such as buildings themselves, which can cast deep shadows protecting not only horizontal ground elements but also vertical surfaces like the walls on adjacent buildings. It also has an added benefit of simultaneously blocking direct solar rays and leaving the sky open, through which heat may escape by ventilation or long wave radiation.

For these reasons,solar radiation is an important aspect in design of urban streetscape and built forms in hot climates. While there is no comprehensive model that can predict the comfort level, recent advances in computational technology, it is possible to determine quantitative measures with varying street and building.

Thus morphologies would be analysed for these parameters using environmental softwares. The parameters that could give a fare idea about the performances of the buildings regarding the issue of surface exposure and street comfort. Some of the measurable parameters addressing these issues are as given below -

I) Incident solar radiation - Facade / roof / streets II) Shadow density III) Sky view factor

Incident solar radiation (wh/m2) Daily average

Facade

Pedestrain level

Roof surface

Simulation examples

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Fig 2.3 Strategic flowchart for measuring incident solar radiation at different layers of urban patch. The diagram shows the simulations of respective layers.

I.Incident solar radiation Radiation is a prominent factor in urban areas, since most of the exchange of energy in urban spaces happens between the building skins and the outer environment. The amount of heat gained by the building surfaces is often the most important parameter for building performance. Since, more the solar gains more is the energy requirement to maintain required comfortable internal temperatures. Similar is the case with pedestrians streets.

The incident solar radiation could be measured on three different layers 1)Facades 2)Roofs 3)Streets Softwares -Ecotect/ Geco/ Grasshopper

Solar radiation (insolation) is measured on surfaces of a model. The calculations use hourly recorded direct and diffuse radiation data from the weather file, based on a specific longitude and latitude. Overshadowing and shading calculations uses the geometry of the building and its surroundings. In the experiment, the whole year is specified as the period of calculation and the given values are average hourly-based. The measurement unit is wh/m² that represents energy in watt hour per square meter.

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Other factors to analyze Pedestrain comfort Shadow density (hr) Daily average

Sky view factor

Simulation examples

Fig 2.4 The diagram shows the parameters involved in analysing the comfort at pedestrain level and their simulations on a random model.

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Fig 2.5 Geometric parameters for calculating the SVF from a point at the centre point of a circular space.

II. Shadow density This parameter is based on the shadows on the ground at specified intervals on a piece of land for a given day or the year. A set of points are set and the average number of hours of shadows is calculated at each point. This parameter has been used by Steemers and Ratti (1999) as an environmental indicator to inform bio-climatic urban design. It helps to weigh the bio-climatic appropriateness of the particular area of a city.

More is the mean shadow density, the better it is in hot-arid regions since they provide protection to pedestrian and horizontal street surfaces from solar radiation. Thus, an increased shadow density can be interpreted as potentially positive.

III. Sky view factor The idea of Sky view factor was introduced in heat transfer literature to model radiative exchange between surfaces offering benefits in urban climatology. It represents the openness of the urban texture to the sky.

For instance , roofs of buildings have a full view of sky vault and thus the svf is 1.0.

This is an indicator that represents the increase/decrease in temperature in the urban context as to when compared with the surrounding rural context- the so-called urban heat island phenomenon. The relation between the SVF and the urban heat island consists of the observation that the smaller the SVF, the higher the temperature of the cities.[2]

Socio-cultural factors (Neighbourhood scale)

destination point

Pedestrain density

Public programs

Shortest walk algorithm

shortest routes

start points

Pedestrain density

Fig 2.6 The diagram shows a simulation of shortest walk along a give path.The cylinders at each node represents pedestrain densities i.e number of people passing each node.

Hierarchy of streets and adjacent building heights

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Fig 2.7 An example of shortest walk based on Dijkstra’s algorithm

B.Socio-cultural factors

Street Network and movement patterns

This framework is based on the requirement that pedestrian movements are important to understand the daily patterns predicting the attraction of individuals at different sites within the local system of interest. These patterns help study the relationship between the pedestrians, and its corresponding effect on the physical layout i.e things like street, squares or religious places and buildings.

The model allocates walker from fixed origins to destinations and in doing so it also takes into consideration various streets, sidewalks, squares and amenity buildings that link the origin and destinations together. The elements of the model reflect ideas of attraction and helps in predicting the traffic models that had been evolved and build on traditions in spatial interaction. Other factors of pedestrian flow like hierarchy of interactive spaces , interactions at nodes and their relative proximity and sizes that bond the tissues together are calculated by the model.

Path generation is based on the Dijkstra algorithm, which helps to resolve the path planning problem not only for path and road networks but also for open spaces. The algorithm takes human preferences or behaviour which is often based on walking conditions into account and can find the path which is best suitable and cost efficient. The results of this approach would be shown in the analysis and design of urban models, where this system is tested on two city patches to understand some relationships between pedestrian movements and the spaces around them.

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A.Design methodologies With the aim of generating climate responsive urban morphologies , the design process will explore a bottom-up evolutionary system as well as experiment with appropriate aggregation logics using generative algorithms , also evaluation of the generations would be a part of the process, thus acting as a feedback back loop everytime a new generation of urban morphologies are made.

The design methodologies would explore the following processes.

I) Genetic algorithms II) Cellular automata III) Network - shortest routes

I. Genetic algorithm This chapter would explore the concept of genetic algorithms and how they are explored as an optimization strategy.

As in the biological origins, the genome of any organism is its transcript. For humans there are just 4 nucleotide types (A, C, G, and T) which are combined into sequences up to 3 billion characters which determine the physical characteristics of each individual. This is also the reason why individuals resemble their mother and father since the genome is a generational inheritance. There would be no thing like evolution without genomes. Inheritance and transformations of genomes from one generation to the next has lead to its natural evolution to optimize the fitness of its creatures which it is often referred as “survival of fittest “.

29 Fig.2.8 Development of wing pigment patterns.In the pictures are subgroups of picture wing species of adiastola and primaeva/attigua [Online] Available at http://www.bio.ilstu. edu/edwards/hawaiiandrosophila/index. shtml[Accessed September 02, 2011]

This field of genomics is often concerned only with living organisms since only they have genomes. However this phenomenon can be potentially tested within the field of architecture considering buildings as genetic organisms, which are an output of series of methods that achieve their ultimate form. This is the base of the concept of genetic algorithm for architectural design.

The genetic algorithm depends on the methods of randomness and to search for a fittest individual of a population. Thus this project is aimed to develop and use genetic algorithm as a means of searching for an optimum design solution for addressing various architectural problems. Also developing a better understanding of genetic algorithms is of prime focus. The attempt would be to find not only a single optimal solution but rather a range of fit options which can be visualized and evaluated by a range of criterias. The most basic steps for Genetic algorithms are the generation of population and its fitness function.

The primary reason of the fitness function would be to find the optimal solution. For that reason it is important to know the phenomenon or quality to be optimised. These can be anything that are related to architectural problems like cost , solar radiation ,shadows ,daylight quality , acoustic quality etc. Basically any parameter that influence the execution of design could be a fitness function.

However there must be a mean to simulate the fitness, and the results must be quantifiable. Thus the fitness should be an output value which represents the performance of the individual which can be evaluated.

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Fig.2.9 The digram(Left)is a simulation of a 2-state cellular automata.The growth rules basically checks the state of its eight neighbours and defines the state of the current cell.

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Fig 2.10 (Top) Experiment by Frei otto for minimal path systems.

[Book] Occupying and connecting,Frei Otto, coverpage

II. Cellular automata (aggregation) Cellular automata are objects that are computable whose characteristics, called states change discretely and uniformly as the states of the neighbours. This system is capable of simulating processes where local action generates local reaction , where order “emerges� as a consequence of applying local rules that generates local changes. Every cell has 8 neighbours, thus every time the state of a cell changes, it checks the current state of the neighbours around it.

This process can be potentially useful for generating patterns where the relationships between the cells are defined. Simulations of urban fabrics and distribution strategies would be explored unlike the conventional methods of zoning. The rules of growth would be the relationships and cultural patterns determined from the study of vernacular patches. Thus, generating a dynamic growth model which can be applied on a 2- dimensional grid on a given urban setting. This process runs through and array of cells and the result is a complex model generated with simple local rules.

III. Network (Shortest routes) There is considerable research evidence that shows that when the journeys are made on foot it serves for wider public interest. For this reason there is a strong need of developing strategies for efficient pedestrian networks that are primarily based on ideas of reduced journey length, walking safer and designing comfortable environment conditions for walking.

Especially in hot-arid climates in the Gulf, the newer urban design projects encourage the dependence on vehicles even for smaller journeys, the reason being the extreme nature of its weather and its focus on vehicular roads rather than pedestrians.

The project will explore various path networks based on concepts and some physical experiment done by Frei Otto in his works. Digital translation of those experiments would be crucial to evaluate the efficiency based on factors of minimum distances and environmental efficiency.

Fig.2.11 A simulation using grasshopper/geco and Ecotect simultaneously for its environmental analysis. [Online] Taiwan architects magazine.Available at http://utos.blogspot.com [Accessed September 12, 2011]

Fig.2.12 A simulation using grasshopper and scripting simultaneously for generating models [Online] Thesis proposal -Wykeithat [Available at] http://wykeithat.blogspot.com [Accessed September 3, 2011]

C. Tools I. Ecotect/Geco

II.Grasshopper

It is an environmental analysis software that allows simulations of building performance from the early stages of design. It comes with detailed analysis of functions with an interactive display that provides the results directly in context of building, It facilitates handling extensive data in a efficient way. This software is extensively used in the project right from analysis stage to design .Its visual nature helps to understand the energy distribution and the performance of buildings.

However, for a genetic algorithm we need software that can communicate with the modelling software and which can provide simultaneous analysis of the same. Geco is plug-in for grasshopper that provides a link between the genetic algorithm and its simultaneous evaluation by eco-

Grasshopper is a visual programming language that runs within the rhinoceros 3d application. Programs are created by dragging components on a canvas where the outputs are then connected as inputs for subsequent components. The generative algorithm’s in the project use this method for creating 3d geometries. It is flexible and allows the user to change the parameters just by changing a few sliders. Grasshopper can be connected with many plug-in for structural analysis and environmental analysis thus it provides a feedback loop every time geometry is analysed making the design process interesting and intelligent. Vb.net within grasshopper is an object-oriented computer programming language that is implemented on the .NET Framework. This component allows generating and running custom made codes within grasshopper. It is a useful component especially in this project since it deals with complex and multiple geometries. The ultimate aim of the project is to also explore these emerging softwares as a new platform for architectural design.[4]

tect. The data received from ecotect is transferred to geco which then is saved in a excel file. Thus this set up makes our evolutionary engine much more efficient and can calculate multiple options within very less time. Geco is a plug-in that links grasshopper with ecotect to achieve real time results as the parameters are changes in grasshopper.[3]

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Vernacular 33

Chapter 3 - Analysis of vernacular city morphologies .............................................................................35-57 3.1 Introduction 3.2 Learning from evolved settlements (hot-arid regions of Middle east) A. Case study 1 - Shibam,Yemen B. Case study 2 - Yazd,Iran 3.3 Comparative analysis 3.4 Conclusions

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3.1 Introduction Throughout the history most of the human settlements were made out of necessity. It is only in the last century that cheap energy and mechanical heating and cooling of the buildings have resulted in a rather inefficient architecture. Vernacular architecture is often considered more sustainable than the contemporary ones since it’s based on limitation of resources and building technologies, thus using ways and solutions that were most efficient. These conditions thus resulted in more sophisticated design techniques that are environmentally more sustainable and culturally adaptive.

With the challenge of meeting the demand of growing urban population, and solving the environmental crisis of 21st century, it seems more than ever necessary to look into the application of vernacular knowledge in creating the kind of architecture and urban environment that are sustainable and culturally appropriate.

The aim of the chapter is to illustrate the identification of principles through the study different morphologies of evolved cities within the context of hot and arid regions of Middle east. This would provide keys insights and lessons for developing a sustainable built environment within the same context.

The research would seek to demonstrate the principles of vernacular design such as orientation, townscape, housing approaches, and land use. Computational algorithm and micro-climate simulations would be performed to understand the physical construct and its subsequent environmental response of the morphologies. This study would conclude by comparing them with newer built models and extraction of certain principles, which would then be optimised by new design methodologies.

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Fig 3.1 Yazd,Iran Aerial view of a typical vernacular morphology with courtyards houses.

[Online] Available at http://kaidashton.blogpot.com [Accessed September 12, 2011].

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Arbil,Iraq

Medina,Saudi Arabia

Morphologies evolved with a major influence of climate and religion on its overall planning.

Fig 3.2 Satellite images of a the morphologies [Online] Available at http://www.wikimapia.org [Accessed September 01, 2011].

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Shibam,Yemen

Yazd,Iran

3.2 Learning from evolved settlements In order to define the parameters to inform the generation of morphologies in hot and arid climatic zones in Middle East, it is essential to study the performances of cities within the same context.This involved the identification of 2 evolved cities that are still functional till date. Cities were carefully chosen with varied design principles to understand different adaptation strategies for the same parameters of influence. To draw some conclusions, similar patches with equal densities are chosen thus the scale of one can be as much 2 times the other. With the available information, 3 dimensional databases of the cities are created with the help of information by google earth.The above pictures are the maps extracted from some traditional cities known for their adaptive architecture principles.

Considering the context, the most influential parameter is the culture and the very harsh nature of the prevailing climatic conditions. The aim would be to understand the principles of building organization and factors like distribution of public, religious and commercial spaces, relationship between streets and building heights, Time of travel from built forms to places of interest, hierarchy of public spaces, pedestrian comfort and energy performance of the buildings which would give a fare idea about the adaptability and environmental sustainability of the city tissues.

The basic strategy would be to analyse urban patches from two cities.

The patch size could vary , however the density of the patch in both the cases would be considered for analysis. The patches would be then divided into 3 scales for investigation i.e a block scale , cluster scale and ultimately neighbourhood scale. Depending on the scale , various factors which would affect its shaping are considered and analysed computationally.

Shadow density The number of hours that one under shadow.

Urban Reflectance (Roof / total surface)

the total surface which indicate

Building scale

UR =

the temperature of urban fabric.

S Roof area S Total Area - S Floor area S: surface area

Cluster scale

analysis strategies

Scales of intervention

More Reflection = Increase Temperture

Surface Volume Ratio : The amount of surface area per unit volume of an object or

Surface Volume Ratio =

S Total Area - S Floor area VTotal

S: surface area V: volume

Orientation: as per local climate

37

Plan

Solar Radiation: the amount of radiant energy roof and facade surface during

Neighbourhood scale Solar access: the amount of light entering the interior spaces

Network: Analysis of the network system spaces based on conceot of shortest walk

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38

Fig 3.3 Aerial view of city of Shibam [Online] Available at http://www.lifestyle. banzaj.pl [Accessed July 18, 2011]

A.Case study 1 - Shibam,Yemen Shibam is a town in Yemen, known for its distinct architecture. The houses of Shibam are all made out of mud brick and about 500 of them are tower houses, which rise 5 to 11 stories high, This architectural style was used in order to protect residents from Bedouin attacks.

General observations The neighbourhood is surrounded by fort walls from all sides with 4 exit and entry points. The public spaces are wide squares at the entry points and in close proximity to all the houses. These squares are surrounded by tall public towers that tend to shade these spaces through a major part of the day. The street network is basically diffusing in width from the square towards the further into residential area. Narrow street widths and corresponding towers generate shadows to reduce the solar exposure on the facades and also shading the corresponding streets.

Population-8000 to 9000 No. of buildings-500 Size -355m by 300m Clusters-4-8 building units Building height- 8-10 stories Floor height- 2-6 m

The heights of the buildings thus seem to have a proportional ratio with the street widths. A series of small interactive spaces can be observed at street intersections the scale of which varies as it goes further from the public spaces. To test these observations, various simulations are performed to gain a relationship between the social patterns,built forms and the climate. This could help to construct some organization strategies to help the design program.

Residential units Religious centres Market (Bazars)

Land use plan

63.9 % Built 36.1 % Open

Open Vs Built spaces

5

3

general observations

1

The diagram shows the relationship between the elements of the fabric

39

1.Primary roads around the patch 2.Four entry points 3.South gate - main entrance,market 4.Religios spaces within close proximity 5.Public spaces around religious buildings 6.Residential houses around public spaces

2

Public spaces on-site resources

4

Residential clusters growth around public spaces

6

Religious/commerical within walking proximity of residential buildings

The diagram shows the relationships extracted from the physical distribution of the built forms.

Residential units Commercial centres Public space (Mosques/parks)

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Input - lines between start point and destination point.In this case residential buildings are destination points and the path network.

Simulation 1 Inputa)Street network b)Starting point – residential buildings c)Destination point – religious buildings

40 Result- The results show the most frequently preferred path network to reach all the points of interest. Simultaneously it calculated the number of pedestrians crossing each node which is represented by the radius of circle.

Simulation 2 Inputa)Street network b)Starting point – residential builings c)Destination point – public spaces

Test 1- Local movement algorithm Inputa) Street network b) Starting point – residential buildings c) Destination point – religious buildings, public spaces Outputa) Shortest routes-lengths of distances from the building to the points of interests b) Pedestrian density at each intersection node on the network

Result – To analyse the results, a random route from a residential building to public space is extracted and the street widths are analysed at each intersection node. A notable hierarchy is observed starting from major public spaces, where the widths are as large as 12m to as low as 1.5 m as it gets closer to more residential areas. Thus there is a diffusion of street widths in the fabric which could have been evolved purely based on the needs to sustain the pedestrian densities. The script also measures the social interaction spaces between the intersections of the roads. A variety of such spaces were observed with some as big as 2500 sq m and the smallest as low as 10 sq.m. Also a similar hierarchy is seen with building heights, more the street width taller is the height which seems as a method to keep the streets shaded throughout the day. The buildings are taller towards the public spaces and shorter as we move into more residential zones of the fabric. Through this example, a clear ratio can be observed between the pedestrian densities, street widths of the network and the corresponding public spaces and building heights.

Detailed path analysis

117

120

9

62

The diagram shows one of the paths generated by simulationg the shortest walk algorithm.The nodes are numbered and the count of pedestrains passing through these nodes as represented as the circles.

118 67

84

The street widhts are then calculated by the algorithm to the closest point on the surrounding buildings.A catalouge is then prepared with the nodes and their corresponding street widths.

4.59

12.1

2

5.87

55

54 173

182 174

Most if the intersections have open areas ranging from 150 - 300 sq.m

Node

Street width

9 117 118 62 120 67 84 55 54 182 174 173

12.1 5.9 4.6 3.15 4.2 3.3 3.4 3.9 2.8 1.85 2.3 2

Types

Area(m2)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

2487 1552 789 280 285 209 284 118 159 154 99 83 14 8 6

41

The street widths shows a gradual change as it moves from a public space to a fartest residential building.

Hierarchy of public spaces from large public squares to smaller social interaction spaces.

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Abstracted model of Shibam that would be subjected to microclimatic tests.

Fig 3.4.(Top left) Street view showing the influence of building morphologies on the street shadows anf even neighbouring buildings.

42

Test 2 - Micro-climate analysis An abstracted cluster of 16 towers is considered for analyzing the microclimate within the fabric of Shibam. The patch size is 44 x 44 m with 1m wide streets between the built forms as seen in most of the residential zones in the fabric. The model was tested in ecotect to evaluate its performance for some of environmental parameters, the values of which would define the livability of the fabrics and some mathematical values which could be later compared to other morphologies. Solar exposure, shadows and lighting conditions, orientation, solar access and surface/ volume figures are the parameters.

The test results showed that the clustered nature of the built forms have many environmental benefits. Shared walls and smaller roof surfaces and low surface volume ratios indicate lesser heat gains. Also tall nature of buildings provide maximum shadow density at pedestrian level , also the buildings tend to shade neighbouring buildings thus further cooling down the facades. However the solar access analysis indicates the lack of light in the interior spaces and the streets under overshadowing in winter.

Incident solar radiation Watt hours per sq.m (wh/m2)

Solar insolation Watt hours (wh)

The diagrams shows the simulation results for measuring incident solar radiation on Ground and facades/roof.

Solar insolation analysis - On ground surface Average value -1309 wh

Incident solar radiation - facades and roof Average value -746 wh/m2

43

Shadow density - 8 hours

The diagram shows the shadow density at a specific given point which in this case is at the center.

Result - 8 hours

Solar access Day light factor(%) Value range 0 - 60 %

The digram shows the persentage of light penetrating within a typical building through windows/ openings at various heights.

At +12.00 M

At +16.00 M

At +20.00 M

At +20.00 M chapter - 3

44 Fig 3.5 (Left) Aerial view of city tissue ofYazd,Iran [Online] Avalable at - http://www.lifestyle.banzaj.pl [Accessed July 18, 2011] Fig 3.6 (Top) A typical neighbourhood parallel to a primary road connecting the city centers.

B. Case study 2 - Yazd, Iran The city of Yazd is located in the center of Iran, in a vast dry rain shadow desert valley overlooked by the Shir Kuh, Iran's highest mountain range.

The extreme climate also evolved architecture predominantly with insulation by mud bricks and thick walls as well as cooling by ventilation structures and courtyard houses.

Architecture type – Courtyard houses Population (patch size)-8000 to 9000 No. of buildings-500 Size -355m by 300m Clusters-4-8 building units Building height- 8-10 stories Floor height- 2-6 m

General observations The city layout is evolved with its main transport routes connecting the peripheries of the city with market places or bazaars in the centre.

Residential neighbourhoods run parallel to the primary roads, with religious and commercial spaces closer to the roads which further converge into residential buildings. The architecture reflects the sophistication in natural heating and cooling systems which also forms the most striking principle of city design.

The heights of the buildings thus seem to have a proportional ratio with the street widths. A series of small interactive spaces can be observed at street intersections the scale of which varies as it goes further from the public spaces. To test these observations , various simulations are performed to gain a relationship between the social patterns,built forms and the climate .

As a general observation , the linear and less dense pattern of growth increases the travel distances and thus it is observed that the streets are enclosed to provide shade and also the publci spaces are decentralized into small spaces to prevent forming large unshaded open spaces.

Residential units

34.5 % Built

Religious centres

65.5 % Open

Public soaces

3

General observations

1

(Left) The diagram shows the relationship between the elements of the fabric

45

1.Primary roads around the patch 2.Markets and religious buildings along the main street

3.Residential buildings growing in single direction 4.Public spaces as pockets within clusters of buildings

2

Main streets

Religious/commerical

4

Residential clusters

The diagram shows the relationships extracted from the physical distribution of the built forms.

Residential units Religious /Commercial centres Main streets

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46

Simulation_1

Simulation_2

Inputa)Street network b)Starting point – residential buildings c)Destination point – religious buildings, public spaces

Inputa)Street network b)Starting point – residential buildings c)Destination point – markets/schools

Test 1-Local movement algorithm Inputa)Street network b)Starting point – residential buildings c)Destination point – religious buildings, public spaces , markets and schools Outputa) Shortest routes-lengths of distances from the buildings to destination b) Pedestrian density at each intersection node on the network.

Result –Similar to the previous example of Shibam, a random street is extracted to analyze the pedestrian densities and its corresponding effects on the street widths.

In this particular case the streets is clearly defined in two sizes. The ones around the peripheries are around 3.5 – 4 m wide whereas as they get into more residential zones, the widths change to around 2 to 1 m wide.

The shortest routes are generally the peripheral streets since the internal routes are not well connected and sometimes run into dead ends.

The relation between the pedestrian density and the building height is not visible in this particular case as all the streets are generally of the same size and so the buildings are generally 1 to 2 storied high. Also the distributed density increases the average walking distances to point of interests. Thus the streets are generally covered to maintain the pedestrian comfort level.

Detailed path analysis The diagram shows one of the paths generated by simulating the shortest walk algorithm.The nodes are numbered and the count of pedestrains passing through these nodes as represented as the circles.

119

127

128

129

119

Node

120

115

119 127 128 129 119 67 120 115 117

Street width 3.9 3.2 3.3 1.3 1.6 1.7 1.3 1.2 0.9

The street widths are almost similar as it moves from a public space to a fartest residential building.

47

No 1 2 3 4 5

Area(m2) 2617 1156 952 430 341

Hierarchy of public spaces from large public squares to smaller social interaction spaces.

In this case the large public spaces are very few and most of them are extensions of the streets itself.

chapter - 3

(Top) Abstracted model of cluster from Yazd that would be subjected to microclimatic tests.

Fig 3.7 (Top left) View of a typical courtyard house in Yazd.[online] Available at http/www.flickr.comphotosmegh.com

48

Test 2 - Micro-climate analysis A cluster of courtyard houses are considered for analysing the quality of microclimate within the tissue of Yazd. The patch size is 90m x 90m , with 16 built forms of 20m x20m each and 1m wide streets between them.

The model was tested under ecotect with the parameters same as for Shibam model, in addition to that a solar access analysis within a typical house is studied to analyse the illumination levels within the houses.

The results showed varied results , some efficient and others not adding much to the efficiency of the houses. The large roof surfaces with almost similar height buildings around it, results in high urban reflectance, increasing the temperature of the fabric. Also such large exposed surfaces absorbs sufficient heat to make the buildings rely on other passive cooling systems. The incident solar radiation on the facades, however show efficient results since most the the facades are shaded by the surrounding buildings.

The solar isolation analysis on the ground show that, even though the streets are narrow, there is enough light falling within the internal spaces of courtyard, thus showing the most efficient results as well as achieving balance between the parameters of shadow density as well as low illumination values on the streets .

Thus overall , this kind of morphology work well both in terms of internal illumination as well as pedestrian comfort. However the units capability to hold a limited number of inhabitants results in a need of many such houses, thus every house being exposed to the environmental parameters more than any other dense urban forms.

Incident solar radiation Watt hours per sq.m (wh/m2)

Solar insolation Watt hours (wh)

The diagrams shows the simulation results for measuring incident solar radiation on Ground and facades/roof.

Incident solar radiation - facades and roof Average value -1129 wh2

Solar insolation analysis - On ground surface Average value -1901 wh

The diagram shows the shadow density at a specific given point which in this case is at the center.

Result - 5 hours

Shadow density - 5 hours

Solar access Day light factor(%) Value range 0 - 60 %

The diagram shows the percentage of light penetrating within a typical building through windows/openings at various heights.

At +0.40 M

At +1.40 M

At +2.40 M

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49

3.3 Comparative analysis

Compact High rise , Shibam

Compact low rise , courtyard houses,Yazd

50

Compact low rise

Open set medium rise

Surface /Volume

Roof surface/ Total surface

Shadow density (Pedestrain level)

Insolation analysis (wh) (Pedestrain level)

Incident solar radiation (wh2) (roof/facades)

8 hours

wh/m2

5 hours

51

wh/m2

5 hours

wh/m2

5 hours

wh/m2

chapter - 3

Discussion of results Parameters Parameters

Preferred valuses Preferred valuses

Surface/Volume Parameters Parametersratio :

Preferred Preferred valuses valuses Surface/Volume ratio : Higher values The amount of surface area per unit Higher values Surface/Volume ratio : ratio Surface/Volume :of volume of anofobject or area collection The amount surface per unit objects. of Higher ratio or causes more of energy volume an object collection Higher values Higher values The of surface area more perarea unit The amount surface per unit loss amount objects.

Higher ratioofcauses energy volume of an object collection of of an or object or collection of loss volume objects.objects.

Higher ratio causes Higher ratio more causesenergy more energy loss loss Surface Volume S Total Area - S Floor area Ratio = Volume Surface S Total Area - S Floor area VTotal Ratio = VTotal SurfaceSurface VolumeVolume S Total Area - SArea Floor S Total - Sarea Floor area Ratio = Ratio = S: surface area VTotal VTotal V: surface volume area S: V: volume S: surface area area S: surface V: volume V: volume Roof / Total surface ratio : Roof / Total surface ratio : ratio to determine the Roof Roof Total/surface Total ratio : ratio : urbanto/reflectance of asurface fabric ratio determine the urban reflectance of a fabric ratio toratio determine to determine the the S Roof area urban reflectance urban reflectance of a fabric of a fabric UR = Roof- area S TotalSArea S Floor area UR = S Total Area - S Floor area S Roof area S Roof area UR = S:UR = Area surface S Total - SArea Floor Sarea Total - Sarea Floor area S: surface area

52

S: surface area area S: surface Shadow density : Shadow density : The number of hours that one specific point Shadow density : that Shadow density : specific point on ground under shadow.

Thethe number ofishours one on the ground is under shadow.

The number of hoursofthat one specific point point The number hours that one specific on the ground is underisshadow. on the ground under shadow.

Lower values Lower values Lower values Lower values

Higher values Higher values Higher values Higher values

Com Com

Benefits Benefits

Com Re .Th Re he .Th Re De he .Th co De he ra co De ra co ra

an increase in the potential for increase natural ventilation , daylighting an in the potential for natural ventilation , daylighting an increase an increase in the potential in the potential for natural for natural ventilation ventilation , daylighting , daylighting But also results in an increase in heat loss during winter heat gain during the But alsothe results in and an increase in heat loss summer which is beneficial dry-arid during the winter and heatingain duringregoins. the But alsoBut results alsoisin results an increase in aninincrease in heat in loss heat loss summer which beneficial dry-arid regoins. during the during winter the and winter heat andgain heat during gain the during the summersummer which iswhich beneficial is beneficial in dry-arid in dry-arid regoins.regoins.

lower values help in reducing the urban reflectance thus reducing lower values help in reducing temperature of a urban fabric. the urban reflectance thus reducing lower values lower help values reducing in reducing the temperature ofinahelp urban fabric. the urban thereflectance urban reflectance thus reducing thus reducing the temperature the temperature of a urban of afabric. urban fabric.

high values are beneficial in hot-arid regions, protecting from radiation high valuespedestrians are beneficial in solar hot-arid regions, protecting pedestrians from solar radiation high values high are values beneficial are beneficial in hot-arid in hot-arid regions,regions, protecting protecting pedestrians pedestrians from solar from radiation solar radiation But excessively higher values may result in overshadowing resulting inmay result But excessively higher values lowovershadowing illumination levels causing in resulting in a But But excessively higher values higher may values may result lack excessively of daylight inlevels pedestrian low illumination causinglevel a result in overshadowing in overshadowing resulting resulting inlevel in lack of daylight in pedestrian low illumination low illumination levels causing levels causing a a lack of daylight lack of daylight in pedestrian in pedestrian level level

Insolation analysis (wh) Intermediate values Pedestrain level (wh) Insolation analysis Intermediate values Pedestrain level This parameter is a analysis measurement Insolation Insolation analysis (wh) (wh) of illuminance value on thePedestrain ground surface.

This parameter islevel a measurement of illuminance Intermediate Intermediate values values Pedestrain level value on the ground surface.

This parameter is a measurement of illuminance This parameter is a measurement of illuminance value on the ground surface.surface. value on the ground

Incident solar radiation (wh2) Facades Incident solar/ roof radiation (wh2) Facades / roof This parameter is a measurement incident Incident Incident solar radiation solar radiation (wh2) of (wh2) solarparameter radiation on the roof and the facades of This is a measurement of incident FacadesFacades / roof / roof built forms. solar radiation on the roof and the facades of This This parameter is a measurement is a measurement of incident of incident builtparameter forms. solar radiation solar radiation on the roof on the and roof theand facades the facades of of built forms. built forms.

Benefits Benefits

Lower values Lower values Lower values Lower values

Balance between over-exposed spaces and onesbetween with extremely low illumination Balance over-exposed spaces levels. and ones with extremely low illumination Balance between between over-exposed over-exposed spaces spaces levels. Balance and ones and with ones extremely with extremely low illumination low illumination levels. levels.

Low values show efficiency in the energy performance of the built forms reducing Low values show efficiency in the energy its need on other resources cooling. performance of thefor built forms reducing its need Low values Low show valuesefficiency show efficiency in the energy in the energy on other resources for cooling. performance performance of the built of the forms builtreducing forms reducing its needits need on otheronresources other resources for cooling. for cooling.

Hi of Hi sin of Hi sin of sin

Res sha Res Ho sha Res illu Ho sha con illu Ho gro con illu typ gro con typ gro typ

T Tin b in Tb in b

I Ii io Io i o

Compact high rise ,Shibam Compact high rise ,Shibam

Compact low rise,courtyard,Yazd Compact low rise,courtyard,Yazd

Compact low rise Compact low rise

Open set medium rise Open set medium rise

Results show that the courtyard type has the highest surface to volume ratio (0.59) as compared to other low and high dense compact forms .These results that although courtyard typesurface had a high potential for(0.59) natural and day low lighting it is also potentially exposed to Results show thatsuggest the courtyard type has the highest to volume ratio as ventilation compared to other and ,high dense compact forms heatresults loss during winter heat gain duringtype summer in hot-arid climate the difference in lighting day and,night temperatures up to 15-19 .These suggest thatand although courtyard had a. However, high potential for natural ventilation and day it is also potentiallygo exposed to Degree C. Thewinter winters areheat mildgain andduring sunny,summer thus the. critical months are the hot months and mitigating the hot months is a must. surfaces heat loss during and However, in hot-arid climate the difference in day and night temperatures goThe up to 15-19 of courtyard could be thus usedand to store heat bythe its thermal mass acting as hot a sink and therefore providing from the temperature Degree C. The winters are mild sunny, thus critical months are the months and mitigating the relief hot months is aextreme must. The surfaces ofstress radiating the heat indoors the surroundings. courtyard could be thus usedand to store heat by its thermal mass acting as a sink and therefore providing relief from the extreme temperature stress radiating the heat indoors and the surroundings.

3

Ranking based on comparative analysis

3

1

1

2

2

4

4

High roof surface to total surface ratio indicates high urban reflection which increasing the temperature of roof the urban fabric at asurface larger scale the compact model seems have the the most efficient ratio High surface to total ratio, indicates highhight-rise urban reflection whichtoincreasing temperature since the building tallerscale and thinner. of the urban fabric atare a larger , the compact hight-rise model seems to have the most efficient ratio since the building are taller and thinner.

1

1

2

2

4

4

3

3

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Results of shadow densities show that model of Shibam, shows the highest values (8 hours). These values therefore indicate potentially efficient shadow conditions on theshow streets.

Results of shadow densities that model of Shibam, shows the highest values (8 hours). These values therefore indicate potentially efficient However even though this case it seems to be an advantaged configuration, sometimes it might result in overshadowing which means low shadow conditions on theinstreets. illumination values. in The courtyard type has theansecond place, configuration, with 3 hours difference in itshadow density this observation seems However even though this case it seems to be advantaged sometimes might result in.However overshadowing which means lowto contradictvalues. the daylight benefits,type so it has should clarified shadow is taken from the street and the illuminance values are an average illumination The courtyard the be second place, with 3density hours difference in shadow density .However this observation seems to of the ground surface (street and courtyards), sobe daylight is actually courtyard and not through the external facade of of the this contradict the daylight benefits, so it should clarified shadow benefited density is from takenthrough from thethe street and the illuminance values are an average type.surface Thus clearly the streets in Compact high-dense form of shibam are most efficient. ground (street and courtyards), so daylight is actually benefited fromthe through the courtyard and not through the external facade of this type. Thus clearly the streets in Compact high-dense form of shibam are the most efficient.

1

1

2

2

2

2

3

3

This parameter seems to contradict the results obtained from shadow density. However the shadow density is only considers the streets and not internal voidsseems like the Theseobtained observations show intermediate values for courtyard house types thus maintaining balance This parameter tocourtyards contradict itself. the results from thus shadow density.

However the shadow density is only considers the streets aand not between over-exposed spaces itself. and under-exposed like the model Shibam and Compact internal voids like the courtyards These observations thus showofintermediate values forlow-rise. courtyard house types thus maintaining a balance between over-exposed spaces and under-exposed like the model of Shibam and Compact low-rise.

2

2

3

3 1

1

4

4

Incident solar radiation is the most important aspect for the overall energy performance of the built forms and achieving more sustainability by reducing its need to rely on other energy cooling.

Shibam model shows the least values the building surfaces are shaded by surfaces Incident solar radiation is the mostresources importantfor aspect for the overall energy performance of the (746 built wh2) formssince and achieving more sustainability by reducing surrounding thus reducingfor the impactShibam to a great extent as compared to other types. its of need to rely onbuilt otherforms energy resources cooling. model shows the least values (746 wh2) since the building surfaces are shaded by surfaces of surrounding built forms thus reducing the impact to a great extent as compared to other types.

1

1

2

2 3

3 4

4

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3.4 Conclusions

Shibam The methods and principles of urban design in Shibam reveal a high level of functionalism. This functionalism is reflected in its adaptation of architecture to its public and private needs. The architecture is based on parameters of security and defence, but moreover, it’s based on adaptation to the local environmental conditions that dictates the forms of the public and private spaces. It is characterized by its compactness of form and efficient utilization of land, thus showing many environmental and social advantages compared to the dispersed form of settlement of Yazd. The compact high dense formation provides easy provision of services and infrastructure such as roads, piped water supply, electricity and sanitary system. Also it promotes walking for short journeys, and public transport for longer journeys making it more energy efficient and economical option.

Yazd Micro climatic factors of a site in which a building is located could optimise much of the building natural energy patterns. Orientation towards the sun, natural ventilation, openness to sky, vegetation and sufficient sunlight in indoors as well as outdoors create a specific microclimate.

An optimum design of these factors influences the energy patterns of the site and the building to a great extent. As in case of Shibam, the city planning shows optimum levels of functionalism and adaptability to the environment. However, the micro climate analysis shows a contrast since it shows cases of overshadowing and insufficient ventilation, thus increasing its dependence on other energy resources to sustain. However on the other hand, Yazd predominantly based on courtyard houses show a higher level of sophistication. The building orientation makes optimum use of heat and sunlight. The large courtyards serve as reservoirs of cool and fresh air, while also helping to spread-out green areas throughout the geographical area in order to redistribute heat equally and avoid its concentration in the centre. The use of natural daylight within the internal spaces is substantial thus increasing the well being of its occupants.

55

Abstracts Looking at the cases of Shibam and Yazd we understand that both the fabrics have evolved respecting its culture and climate. Deriving from the models, we understand that compactness in conjunction with porosity (courtyards) in the built form might provide a better solution towards the issues of environmental performance and urban planning in hot-arid regions.

Clustering of buildings, porous elements within the built forms, orientation, program distribution and hierarchy in the social structure and spaces are some of the abstracts that would be taken as starting concepts and the aim would be to achieve similar performances.

Parametric design tools would be used to develop generative models based on these abstracts and a series of urban models would be developed. These would then be evaluated to check the performance in relation with the vernacular morphologies. Thus attempting to achieve a contemporary scenario at the same time respecting the traditionality of the context.

Abstracts from studies (starting ideas for design)

Clustering of buildings Porosity within built forms (courtyards) Program distribution patterns Hierarchy of streets and social spaces

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genetic algorithm Chapter 4 - First experiments for generations of urban patches.........................................59-91 4.1. Introduction 4.2. Genetic process A.Block generation B.Patch generation 4.3. Conclusions

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58

4.1 Introduction This chapter explores the tools and design methodologies described in chapter 2 as the next phase in developing this project. The concepts are based from the site analysis and inspirations from vernacular evolved cities .The idea of genetic algorithms would be explored to achieve different variations with similar density urban patches. This strategy addresses the simple idea that different organizations show different cohesive properties and performances. To test these properties, various physical and digital experiments were conducted to measure environmental performances according to the site conditions. This chapter is crucial to setting up a computational engine for application of the process on a physical site.

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4.2 Genetic process The genetic process begins with evolution of generations based on certain mutation strategies. Each mutation is aimed to increase the efficiency of the primitives in terms of its environmental performance.

The generations in this research are basically evolved by clustering certain primitive buildings which are parametrically derived by generative model based on associations and ratios between spaces and built forms abstracted from vernacular morphologies. They are then clustered with aggregation strategies , which would produce large number of architectural blocks with same type and number of buildings but with different arrangements. Furthermore mutations are performed to produced more optimised forms. The final result of this experiment would be evolution of a few fit clusters which could be applied on the program site where various aggregation logics would be explored. Thus the overall process can be broken down into three scalesI. Building scale - Block generation

In this scale, a generative model is designed to generate buildings based on principles abstracted from study of vernacular houses, incorporating prevailing needs of housing types in contemporary cities of Middle east.

II. Cluster scale - Plot generation Based on given density of the urban patch, the individual buildings generated in the previous step would be clustered into a compact form. A genetic algorithm is developed based on the logic of “Sudoku� to generate variations within the same buildings of a given density, but with different orientations and interrelations. These options would then be measured for their livability and environmental performance to shortlist a few fit variations. The short listed options would then be optimised by physical changes on the individual buildings and just as the last step would be again subjected to evaluation to achieve the best possible option, for a given density and at a given location.

III. Neighbourhood scale

The above process would define the possible morphologies which need to be aggregated to form a patch of a neighbourhood scale. To achieve this, a parametric model is developed that would distribute these clusters based on the relation between the public spaces to the location of the cluster, trying to achieve maximum compactness.

Site specific strategy Neighbourhood scale

Plot generation

4 plots

4 plots Same density patches 4×4 Blocks

Block scale (12m*12m -51m *51m )

Non site specific strategy Block scale/patch scale

Sudoku logic 12 Patch Arrangements

Generation 12 Individuals

Fitness criterias In hierarchy (1st evaluation)

Evaluation(1)

1

- Average solar radiation on the facade - Insolation analysis at pedestrian level

2

- Average solar radiation on roof

Attraction / Repel Factors

Public space generation (nodes as seed point) Land use distribution

Cluster scale (12m*12m -51m *51m ) Mutation(1)

Pedestrian intensity (Around the site)

Nodes generation with wooly path system

Generation 1 12 Individuals

Random height Modification

Site

Residential Density distribution Corresponding density patch

dictates patch orientation

Self-organization Circle packing (within the patch)

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24 individuals

16 14

12

Mutation(2) Slab movement Fitness criterias (2nd evaluation)

best 12 patches evaluated by 1st fitness criteria

Evaluation(2) 12

- Total average solar radiation 8 - Total amount of terraces - Wide terraces area/Total terraces area 4 The 4 fittests

Neighbourhood scale (12m*12m -51m *51m )

3

- Open surface/total surface - Sky view factor

fittest patch in location patch settelment chapter - 4

Fig 4.1.(Left)A block type generated based on spatial distribution of a hierarchy of public,semi public and private spaces.

Fig 4.2.(Left)The width of the courtyard and the heights of the built forms are proportional such that it always keeps them under shadows.

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Fig 4.3.(Bottom) A series of varied sizes of courtyard houses studied to analyse the association of courtyards and the built forms around it.

A.Block generation - courtyards As the start point of the generative process, the basic unit / geometry was chosen to be the primitive. A cube of 5m x 5m which is the basic scale of the block. In order to produce the typology variations, a generic growth model is prepared which is basically abstracted from the study of a series of courtyard housed from the analysed tissue of Yazd. The aim is to create a hierarchy of public and private spaces within different scales of building typologies. The building typologies are basically based on the current housing patterns within urban areas of Middle East. Growth of the model is controlled by two parameters i.e. the size / percentage of courtyard spaces and the area of the block. Based on the area, the blocks are generated in a way that they always generate private and semi-public spaces within. The height of the blocks is designed to always shade the courtyard spaces. Thus setting up a basic housing model fairly responsive to issues of solar exposure and ventilation.

Vb scripting helps to provide freedom to control the total percentage of open spaces which can vary as per the context. The system helps to achieve basic forms that are generated starting from a cube to medium scale complex built forms. However it being an associative process, more complex built forms could be experimented to achieve optimised results even at the scale of a single building. For the first experiment, the forms are kept simple since it is necessary to test the working of the whole system considering the limitations of computation.

63

Fig 4.4.(left) Parametric model for generic growth of housing blocks.The model grows as per the input area into courtyard typologies based on creating hierarchies of public to private spaces.The size of courtyard thus achieved define the height of buildings around it such that the courtyard is always under shadow.

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A.Block generation - incorporating new homes The design for building typologies is derived from the current housing patterns that ranges from apartment buildings to villas with different ranges of units from single room houses to 2-3 to 4 bedroom houses and also independent villas. The attempt would be to incorporate these patterns in the design and not a afterthought as generally observed in many bottom-up city design precedents. Also the courtyard spaces are distributed as the blocks increase in footprint to equally divide private spaces and thereby increase natural ventilation throughout and not centralized.

The algorithm is informed to grow a particular building only till a extent where it cannot provide atleast one side of the room facing ,either the courtyard or the street. The diagram (Right) shows the floor plans of the units generated. Also these buildings are designed to be flexible to accommodate different variations of house types based on the location of its intervention.

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Palette of generated building types which would then be group based on a given density.

A 4x4 sudoku arrangement ,with digits so that each column, each row, and the bottom left 2×2 sub-grid that compose the grid contain all of the digits from a to d. For this particular case , the bottom left grid values are fixed.

B.Patch generation The preliminary stage involves generation of patches i.e. agglomerations of smaller blocks as achieved in the generic growth model of courtyard houses. However the blocks need to be clustered in a way that they produce various options to have multiple relative models with the same set of buildings. This lead to a research in different aggregation strategies which would provide us flexibility and also make the process more geometric/logical. The need of such a logic lead to the concept of “Sudoku“.

Sudoku is a logic-based number-placement puzzle. A completed Sudoku grid is a special type of Latin square with the additional property of no repeated values in any of the 9 blocks of contiguous 3×3 cells. The objective is to fill a 9×9 grid with digits so that each column, each row, and each of the nine 3×3 sub-grids that compose the grid contain all of the digits from 1 to 9. The puzzle setter provides a partially completed grid, which typically has a unique solution.[5] The Sudoku considered for experiment is 4 x 4, to achieve a cluster scale patch of 16 buildings. The bottom left set of cells are basically given with the starting numbers representing the type of block from

the palate of building types generated in the previous experiment. Thus each sudoku in this case, would generate 12 different permutations and combinations of the same 4 buildings but in varied orientations and also having different cohesive properties.

The constraint of the process is the density, which always remains the same throughout the generative process. Thus there is a possibility of achieving similar densities with different combination of building blocks as in the case considered for this experiment. In some of the cases two different combinations are possible for the same density, thus two generations are produced, to analyse which arrangement is more suitable the generations are then applied to the next mutation that is random height modifications to further intensity the mutual effects of the buildings in terms of solar exposure and associative shading and pedestrian comfort levels. The two generations thus produce 24 individuals representing a specific density; these options however have some organisational difficulties explained in the next topic.

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Evaluation 1

Self-organization (packing) The first two mutations of the generative process resulted in 24 individuals. These options however had its own constraints since it’s basically being a grid type formalized. As it can be seen in the example the output is a cluster with large overlaps. This could be resolved by self-organization strategy of packing, this would help the patch to have a combination of open spaces as well it would also minimize large overlaps.

Thus a set of inscribed and circumscribed circles are defined for all the building blocks. Based on the size of the block, various combinations of circles are made to balance the open spaces, with the large ones having the circumscribed circles leaving a larger set back area and accommodating the smaller ones around it.This strategy also helped us to get away from the formalized grid and also started generating different figure-ground ratios with its emergent spaces and different foot prints.

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Fig 4.5 Sample cluster organization generated by the first mutation i.e sudoku where values of 4 cells of left bottom are predefined.

70 Fig 4.6 Sample cluster organization generated by the second mutation of random height modification ,maintaining the same overall volume of the cluster .

Generation 1.1 The first generation of clusters is achieved by mutation of re-arrangement, and random height modification. This is to get more variations within the patch and thus also to enhance the cohesive properties within the buildings. The rule set for the process of random height modification is that the building should be above 8m height and in the whole process, the overall volume of the patch always remains the same. If the volume of one of the buildings is increased, it is balanced by reducing the same for another one. This process thus gives 12 unique option with varied performance values and thus varied urban patches.

Patch generation

Block generation

Mutation strategy

Generation (1)

circle packing

1.1

1.2

Evaluation(1) fitness(1)

fitness(2) selection

fitness(3)

Generation (1) fit patches Mutation

Generation (2)

Evaluation(2) fitness(1)

fitness(2)

Generation 1.1 Volume = 16250 mÂł

1st mutation Block rearrangement

Gen1.1.1

Gen1.1.2

Gen 1.1.3

Gen1.1.4

Gen1.1.5

Gen1.1.6

Gen1.1.7

Gen1.1.8

Gen 1.1.9

Gen1.1.10

Gen1.1.11

Gen1.1.12

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2nd mutation Height modiďŹ cation

Gen1.1.1

Gen1.1.2

Gen1.1.3

Gen1.1.4

Gen1.1.5

Gen1.1.6

Gen1.1.7

Gen1.1.8

Gen1.1.9

Gen1.1.10

Gen1.1.11

Gen1.1.12

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Fig 4.7 Sample cluster organization generated by the first mutation i.e sudoku where values of 4 cells of left bottom are predefined.

72 Fig 4.8 Sample cluster organization generated by the second mutation of random height modification ,maintaining the same overall volume of the cluster .

Generation 1.2 This generation represents an alternative to the generation 1.1, representing similar volumes ,with a different set/combination of buildings. The process of generating this population is thus exactly similar to the previous one .

(2nd evaluation) Fitness criteria (2nd evaluation) Generation (1) fit patches Mutation strategy circle packing 1.1 1.2 fitness(1) fitness(2) fitness(3) Mutation Generation (1) selection Patch generation Generation (1) Block generation Evaluation(1) fit patches Mutation strategy circle packing 1.1 1.2 fitness(1) fitness(2) fitness(3) Mutation selection

Block generation

Patch generation

Generation (1)

Generation (2)

Evaluation(1)

Evaluation(2) fitness(1)

Generation (2)

fitness(2)

Evaluation(2) fitness(1)

fitness(2)

Generation 1.2 Density Volume = 16800 m³ Generation 1.2 Density = 16800 m³

1st mutation Block rearrangement 1st mutation Block rearrangement

Gen1.2.1

Gen 1.2.3

Gen1.2.2 Gen1.2.1

Gen1.2.7

Gen 1.2.3

Gen1.2.4

Gen1.2.8

Gen 1.2.9 Gen 1.2.9

Gen1.2.10 Gen1.2.10

Gen1.2.8 Gen1.2.7

Gen1.2.4

Gen1.2.2

Gen1.2.6

Gen1.2.5

Gen1.2.6

Gen1.2.5

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Gen1.2.12 Gen1.2.12

Gen1.2.11 Gen1.2.11

2nd mutation 2nd mutation Height modification Height modification

Gen1.2.1 Gen1.2.1

Gen1.2.2Gen1.2.2

Gen1.2.3 Gen1.2.3

Gen1.2.4 Gen1.2.4

Gen1.2.5 Gen1.2.5

Gen1.2.6Gen1.2.6

Gen1.2.7 Gen1.2.7

Gen1.8 Gen1.8

Gen1.2.9

Gen1.2.10 Gen1.2.10

Gen1.2.11 Gen1.2.11

Gen1.12 Gen1.12

Gen1.2.9

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Generation 2

Evaluation 1 - Fitness 1 After the two generations, the first evaluation is brought into consideration. The aim of evaluation is to measure the fitness of the generations to shortlist a few best ones for further investigation. The fitness Criteria at this scale being environmental morphological indicators as discussed in chapter 2 namely; solar radiation on streets roofs and façade, sky view factor and open vs. built space ratio. The values obtained from this test would determine the environmental efficiency and livability of the blocks.

Based on the hierarchy of impact on the overall efficiency of the building, the parameters are organized in different scales, the first being the incident solar radiation on the facades of the buildings and the average solar access on the streets In this case ,16 options would be shortlisted based on the measurements of these factors.

The experiment was done on ecotect with the basic inputs of climate and the time zones for desired calculations. The generations and calculations were done by vb-script which was directly connected to ecotect, capable of storing data in excel sheets simultaneously. It was realized that the process is time consuming considering the scale and multi- parametric checks. However the output showed a range of values, for the minimum –maximum vary up to 20%, thus resulting in drastic variations in most of the parameters.

Orientation - It is a most crucial element that controls the environmental values. Thus,before the evaluation , the orientation should come as an input based on the site or other factors like wind direction,streets etc. For this experiment , we consider the patch to be oriented towards north.

Patch generation

Block generation

Mutation strategy

Generation (1)

circle packing

1.1

1.2

Evaluation(1) fitness(1)

fitness(2) selection

fitness(3)

Generation (1) fit patches Mutation

Generation (2)

Evaluation(2) fitness(1)

fitness(2)

Roof

Calculation: Incident solar radiation (roof and facade) Solar access refers to the availability of incident solar radiation (insolation), on surfaces of a model, using hourly recorded direct and diffuse radiation data from the weather file, based on a specific longitude and latitude.

Weather file : Abu-dhabi (lat:24.4 lng:54.7) Period of calculation: Annually Measurement unit: watt hour per square meter (wh/m²) Facade

Average solar radiation (wh/m2) Gen1.1

Gen1.1.1 1115

Gen1.1.2 1161

Gen1.1.3 1219

Gen1.1.4 1109

Gen1.1.5 1104

Gen1.1.6 1101

Gen1.1.7 1166

Gen1.1.8 1131

Gen1.1.9 1120

Gen1.1.10 1129

Gen1.1.11 1119

Gen1.1.12 1203

Gen1.2

Gen1.2.1 1230

Gen1.2.2 1344

Gen1.2.3 1270

Gen1.2.4 1417

Gen1.2.5 1389

Gen1.2.6 1440

Gen1.2.7 1404

Gen1.2.8 1428

Gen1.2.9 1315

Gen1.2.10 1343

Gen1.2.11 1370

Gen1.2.12 1413

Average solar radiation on roof (wh/m2) Gen1.1

Gen1.1.1 3978

Gen1.1.2 4135

Gen1.1.3 4050

Gen1.1.4 4028

Gen1.1.5 4014

Gen1.1.6 3795

Gen1.1.7 3962

Gen1.1.8 3915

Gen1.1.9 3993

Gen1.1.10 3970

Gen1.1.11 3920

Gen1.1.12 4010

Gen1.2

Gen1.2.1 4019

Gen1.2.2 4065

Gen1.2.3 4032

Gen1.2.4 4245

Gen1.2.5 4177

Gen1.2.6 4039

Gen1.2.7 4270

Gen1.2.8 4289

Gen1.2.9 4099

Gen1.2.10 3988

Gen1.2.11 4160

Gen1.2.12 4217

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Average solar radiation on the facade (wh/m2) Gen1.1

Gen1.1.1 786

Gen1.1.2 845

Gen1.1.3 883

Gen1.1.4 812

Gen1.1.5 828

Gen1.1.6 721

Gen1.1.7 731

Gen1.1.8 825

Gen1.1.9 816

Gen1.1.10 783

Gen1.1.11 837

Gen1.1.12 878

Gen1.2

Gen1.2.1 789

Gen1.2.2 1102

Gen1.2.3 864

Gen1.2.4 975

Gen1.2.5 1120

Gen1.2.6 1095

Gen1.2.7 1088

Gen1.2.8 996

Gen1.2.9 827

Gen1.2.10 899

Gen1.2.11 943

Gen1.2.12 1100

Calculation: Incident solar radiation (pedestrian level) Weather file : Abu-dhabi (lat:24.4 lng:54.7) Period of calculation: from Mid-May to Mid-August Measurement unit: watt per hour (wh)

Insolation analysis (pedestrian level) (wh) Gen1

Gen1.1 2922.98

Gen1.2 2882.07

Gen1.3 3422.29

Gen1.4 3101.32

Gen1.5 3189.92

Gen1.6 2905.08

Gen1.7 2778.42

Gen1.8 2777.16

Gen1.9 2521.08

Gen1.10 2698.14

Gen1.11 2938.95

Gen1.12 2672.22

Gen2

Gen2.1 2950.62

Gen2.2 2869.24

Gen2.3 3342.41

Gen2.4 2655.03

Gen2.5 2805.98

Gen2.6 2913.47

Gen2.7 2715.29

Gen2.8 2694.55

Gen2.9 3098.93

Gen2.10 3059.84

Gen2.11 3113.19

Gen2.12 2893.18

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Evaluation 1 - Fitness 2 and 3 The next level of evaluation include the solar radiation levels on the roof.

This factor would further shortlist the list from 16 to 14. The next phase of evaluation would be the open space/total area and sky view factor.

These factors would also determine the spatial qualities and livability of the urban patches, thus short listing 12 best options which would undergo other mutation strategies.

Patch generation

Block generation

Mutation strategy

Generation (1)

circle packing

1.1

1.2

Evaluation(1) fitness(1)

fitness(2) selection

fitness(3)

Generation (1) fit patches Mutation

Generation (2)

Evaluation(2) fitness(1)

fitness(2)

Calculation: Average sunlight hours ( pedestrian level ) calculates sunlight hours values over visible points in the analysis grid and takes the average of all.

Weather file: Abu-dhabi (lat:24.4 lng:54.7) Period of calculation: from Mid-May to Mid-August Measurement unit: hour(hr)

Average sunlight hours (pedestrian level)(hr) Gen1.1

Gen1.1.1 3.49

Gen1.1.2 3.42

Gen1.1.3 4.16

Gen1.1.4 3.76

Gen1.1.5 3.85

Gen1.1.6 3.46

Gen1.1.7 3.29

Gen1.1.8 3.31

Gen1.1.9 2.94

Gen1.1.10 Gen1.1.11 3.18 3.49

Gen1.1.12 3.15

Gen1.2

Gen1.2.1 3.54

Gen1.2.2 3.41

Gen1.2.3 4.04

Gen1.2.4 3.12

Gen1.2.5 3.32

Gen1.2.6 3.49

Gen1.2.7 3.22

Gen1.2.8 3.17

Gen1.2.9 3.7

Gen1.2.10 Gen1.2.11 3.67 3.73

Gen1.2.12 3.44

Calculation: Open space/ Total spaces

open space refers to the volume of void space between the buildings and total space is the volume of whole patch

77

Open space/ total space Gen1.1

Gen1.1.1 39%

Gen1.1.2 36%

Gen1.1.3 37%

Gen1.1.4 29%

Gen1.1.5 42%

Gen1.1.6 25%

Gen1.1.7 35%

Gen1.1.8 31%

Gen1.1.9 26%

Gen1.1.10 29%

Gen1.1.11 30%

Gen1.1.12 29%

Gen1.2

Gen1.2.1 39%

Gen1.2.2 35%

Gen1.2.3 52%

Gen1.2.4 34%

Gen1.2.5 37%

Gen1.2.6 34%

Gen1.2.7 40%

Gen1.2.8 35%

Gen1.2.9 43%

Gen1.2.10 49%

Gen1.2.11 42%

Gen1.2.12 36%

SVF L H

ß

Calculation: Sky View Factor ( SVF )

The Sky View Factor of an urban canyon is closely related to its aspect ( H/W ) ratio , as it also describes the cross-sectional properties of the canyon. The SVF is the proportion of the sky dome that is seen by a surface, either from a particular point on that surface or integrated over its entire area.

W SVF

H ß

r

Sky view factor Gen1.1

Gen1.1.1 0.449

Gen1.1.2 0.476

Gen1.1.3 0.508

Gen1.1.4 0.454

Gen1.1.5 0.478

Gen1.1.6 0.451

Gen1.1.7 0.453

Gen1.1.8 0.43

Gen1.1.9 0.448

Gen1.1.10 0.432

Gen1.1.11 0.477

Gen1.1.12 0.436

Gen1.2

Gen1.2.1 0.469

Gen1.2.2 0.422

Gen1.2.3 0.481

Gen1.2.4 0.42

Gen1.2.5 0.386

Gen1.2.6 0.439

Gen1.2.7 0.39

Gen1.2.8 0.402

Gen1.2.9 0.39

Gen1.2.10 0.4

Gen1.2.11 0.458

Gen1.2.12 0.363

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Evaluation 1-Sorting After the parameters are calculated, the values for each patch are listed together. The values are arranged in an ascending order regardless to the patch it belongs to. The arrangement of the lists of values also represents the hierarchy of importance of the parameters. Lines are then drawn from all the patches to their respective values. Then starts the process of short listing based on the overall performance of the patches.

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80

Evaluation 1- Selection Once the lists lists are sorted, graphs are made along with their mean values. Depending on its difference from the mean value, the options are short listed in 3 steps In the first step; best 16 options are selected based on the values of solar radiation on facades and solar insolation. The next step removes 2 more options based on values of solar radiation since the values are more or less similar in most other cases. Sky view factor and open/built surface ratios are considered to finally shortlist 12 options which are the fittest amongst the 24 options. These 12 individual are kept for their benefiting role in urban micro climate. These options would then be subjected to next level of mutations to optimise their performance and add more functionality and layers of spaces which will be again subjected to the next level of evaluation to extract 4-5 fittest options as an output of this generative process.

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chapter - 4

Sample cluster organization generated by the first mutation of random height modification.

82

Sample cluster organization generated by the second mutation of random slab movement for buildings above 8m.

Generation 2 With the 12 individuals from the previous generations, several mutations are conducted for the second time to produce generation 2. In the second generation, each individuals are evolved by the movement of the floor slabs that would tend break the facades thus creating self-shading surfaces between different floors. The input for the modelling software is basically a range of movement that is set from 0 – 3 meters. However the movement is restricted only towards the south and west facades with an idea of created self-shaded facades on the south side and in the mean while generating terraces on the east and north facades. The 12 individuals together form a new generation that are still limited with the associations gained in the previous mutations, but the conditions and performance are getting more complicated.

Patch generation

Block generation

Mutation strategy Generation 1.1

Generation (1)

circle packing Generation 1.2

1.1

1.2

Evaluation(1) fitness(1)

fitness(2)

fitness(3)

Generation (1) fit patches Mutation strategy

Generation (2)

Evaluation(2) fitness(1)

fitness(2)

selection

Gen 1.1.10

Gen 1.1.7

Gen 1.1.12

Gen 1.1.9

Gen 1.1.2

Gen 1.1.8

Gen 1.1.11

Gen 1.2.1

Gen 1.1.6

Gen 1.1.4

Gen 1.1.1

Gen 1.2.9

een patch

Mutation (2) General strategy - floor slab movement towards the south ,west or both simultaneously.

south / west

The mutation strategy of the second generation would be applied to the 12 fittests of generation (1). This mutation of horizontal movement of slabs tends to reduce the total amount of solar radiation on the most exposed facades.

83

h 8m h=8

h>8

h>8

a a

Specifications-

h<8

the slab movement would be a random figure between 0 to 3 meters along -x and -y, applied to the blocks with more than 8m height.

The slabs of blocks within the proximity of 1m after the mutation should be merged.

a a

a -X

0 m to 3m

a≥1m 0 m to 3m -Y

a

if 0 <a< 1m

slab attachment

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Plot generation

Patch generation Mutation strategy Generation 1.1

circle packing Generation 1.2

Generation (1) 1.1

1.2

Evaluation(1) fitness(1)

fitness(2) selection

fitness(3)

Generation (1) fit patches Mutation

Gen 2.1

Gen 2.5

Gen 2.9

Gen 2.2

Gen 2.6

Gen 2.10

Generation (2)

Evaluation(2) fitness(1)

fitness(2)

85

Gen 2.3

Gen 2.7

Gen 2.11

Gen 2.4

Gen 2.8

Gen 2.12

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Sample cluster with terraces and generative spaces for balconies due to random slab movement.

Evaluation 2 After the second generation, the second evaluation is brought into consideration. The aim of evaluation is to measure the effect of the mutations that are expected to perform better in terms of its environmental efficiency and at same time create interactive spaces within the buildings and also between the buildings. The fitness criterias at this scale are to measure the impact of the mutations on the incident solar radiation and also the measure of accessible terraces/balconies within the buildings. The values obtained from this test would determine the efficiency and quality of spaces within the buildings. Similar to evaluation 1, the values are again placed in an ascending order; the primary criteria of the short-listing being the average total solar radiation. 8 options are short-listed based on this criteria .

The next set of criterias evaluates the potential green spaces that are generated due to the slab movement. These spaces can be potentially used as balconies or in some cases terraces. Also in some instances the spaces are too small to be accessible. Thus it was important to separate these spaces which had potential to be converted into terraces or other programmatic spaces like small terrace gardens. The criteria set for such segregation is a minimum width of 2.5m. Thus based on the total percentage of spaces more than 2.5m, amongst the total terraced spaces, 4 options are short listed.

generation PlotPlot generation

Patch generation Patch generation

Generation Generation (1) (1)

Mutation strategy circle packing Mutation strategy circle packing Generation Generation 1.1 Generation Generation 1.1 1.2 1.2 1.1 1.11.2 1.2

Generation Generation (1) (1) fit patches fit patches fitness(1) fitness(2) fitness(2) fitness(3) fitness(3) Mutation fitness(1) Mutation Evaluation(1) Evaluation(1)

Generation Generation (2) (2)

Evaluation(2) Evaluation(2) fitness(1) fitness(2) fitness(2) fitness(1)

selection selection

Calculation: Incident FIG.1- Calculation: Incidentsolar solarradiation radiation ( roof and facade )

Weather file : Abu-dhabi (lat:24.4 lng:54.7) Period of calculation: Annually Measurement unit: Weather file : Abu-dhabi (lat:24.4 watt hour per square meter (wh/m²) lng:54.7)

Period of calculation: Annually Measurement unit: watt hour per square meter (wh/m²)

Average AVERAGE radiation (wh/m2) SOLARsolar RADIATION (Wh/m2) Gen.2 Gen.2

Gen Gen 2.1 2.1 10161016

Gen Gen 2.2 2.2 Gen Gen 2.3 2.3 Gen Gen 2.4 2.4 Gen Gen 2.5 2.5 Gen Gen 2.6 2.6 Gen Gen 2.7 2.7 Gen Gen 2.8 2.8 Gen Gen 2.9 2.9 Gen Gen 2.102.10 Gen Gen 2.112.11 Gen Gen 2.122.12 10711071

11021102

958 958

10291029

979 979

10321032

10591059

953 953

11191119

10301030

11891189

Calculation: Total FIG.1- Calculation: Totalamount amountof ofterraces terraces Terracescaused causedby bymoved movedslabs slabswill willbe be Terraces measuredininsquare squaremeter. meter measured Measurement unit: square meter (m²) 87 Measurement unit: square meter (m²)

amount of terraces(m²) (m²) TOTAL Total AMOUNT OF TERRACES Gen.2 GenGen 2.1 2.1 GenGen 2.2 2.2 GenGen 2.3 2.3 GenGen 2.4 2.4 GenGen 2.5 2.5 GenGen 2.6 2.6 GenGen 2.7 2.7 GenGen 2.8 2.8 GenGen 2.9 2.9 GenGen 2.102.10 GenGen 2.112.11 GenGen 2.122.12 Gen.2 3277 28712871 3277

2521 2521

3439 3439

2933 2933

3203 3203

2447 2447

2939 2939

3050 3050

2291 2291

3797 3797

2970 2970

Calculation: 2.5m FIG.1- Calculation: 2.5mWide Wideterraces terracesarea area// total terraces area total terraces area

2.5 meter wide terraces would be considered as the potential green space areas 2.5 meter wide terraces would be whose ratios to total terraces will be calculated.

considered as thE potential green space areas whose ratios to total terraces will be calculated.

2.5mTERRACES wide terraces / total terracesAREA area 2.5m WIDE AREAarea / TOTAL TERRACES Gen.2 GenGen 2.1 2.1 GenGen 2.2 2.2 GenGen 2.3 2.3 GenGen 2.4 2.4 GenGen 2.5 2.5 GenGen 2.6 2.6 GenGen 2.7 2.7 GenGen 2.8 2.8 GenGen 2.9 2.9 GenGen 2.102.10 GenGen 2.112.11 GenGen 2.122.12 Gen.2 44%44%

51%51%

43%43%

33%33%

45%45%

42%42%

37%37%

48%48%

37%37%

32%32%

55%55%

45%45%

chapter - 4

Patch generation

Plot generation

Mutation strategy Generation 1.1

circle packing Generation 1.2

Generation (1) 1.1

1.2

Evaluation(1) fitness(1)

fitness(2) selection

fitness(3)

Generation (2)

fit patches Mutation Plot generation

Evaluation(2) fitness(1) fitness(2) Generation selection (1)

Patch generation Mutation strategy Generation 1.1

circle packing Generation 1.2

1.1

1.2

Evaluation( fitness(1)

fitness(2 selectio

Generation (2)

Hierarchical Structure of Fitness criterias

Hierarchical Structure of Fitness criterias

12 selection

1 - Average solar radiation (wh/m²)

1 - Average solar radiatio

8

2 - Total amount of balconies (m²) - 2.5 m width balconies area/total balconies area

2 - Total amount of balco - 2.5 m width balconie

4 fit patches

Average solar radiation (wh/m²)

88

Gen 2.9 Gen 2.4 Gen 2.6 Gen 2.1 Gen 2.5 Gen 2.11 Gen 2.7 Gen 2.8 Gen 2.2 Gen 2.3 Gen 2.10 Gen 2.12

953.0 958.0 979.0 1016.0 1029.0 1030.0 1032.0 1059.0 1071.0 1102.0 1119.0 1189.0

Total amount of terraces (m²)

2.5m wide terraces area / total terraces area

2291.0 2447.0 2521.0 2871.0 2933.0 2939.0 2970.0 3050.0 3203.0 3277.0 3439.0 3797.0

Gen 2.9 32 Gen 2.4 33 Gen 2.6 37 Gen 2.1 37 Gen 2.5 42 Gen 2.1143 Gen 2.7 44 Gen 2.8 45 Gen 2.2 45 Gen 2.3 48 Gen 2.1051 Gen 2.1255

Average solar radiation (wh/m²)

Total amo terrace

953.0 958.0 979.0 1016.0 1029.0 1030.0 1032.0 1059.0 1071.0 1102.0 1119.0 1189.0

2291.0 2447.0 2521.0 2871.0 2933.0 2939.0 2970.0 3050.0 3203.0 3277.0 3439.0 3797.0

Evaluation 2 -Selection 2nd set Best 8 patches

of fitness criteria

The valuesbyfrom evaluation to are sorted based on the parameters evaluated 1st the 2nd (potential have green space) fitness criteria applied on this generation. The bar charts represent the relative values in sq.m in percentage and the mean average .Based on the preferred criteria of higher or lower 37% 1 3050 Gen 2.9 values,best 8 options are selected at the first stage. These options are 33% 2 3439 Gen 2.4 then 42%The1 second set of fitness crite3 analysed 3203 spaces.

Gen 2.6 for their generative 4 3277 Gen 2.1 2 options since they have ria would further reduce the numbers 44% to 4 best 45% 3 5 2933 Gen 2.5 relatively similar values . 3797 55% 4 6 Gen 2.11 37%of this genetic process which 7 diagrams 2447 The the output Gen 2.7 thus represent 48% 8 2939 Gen 2.8 are appropriate in terms of their relative energy performance and at the same time producing interesting generative spaces.

However,these options show more or less similar fitness values,thus for its application a program site,it would be important to choose the options based on the orientation and its application on site. Also,as a future exploration the quality of spaces could be evaluated to further choose the most appropriate alternative.

Best 8 patches evaluated by 1st fitness criteria

1 2 3 4 5 6 7 8

Gen 2.9 Gen 2.4 Gen 2.6 Gen 2.1 Gen 2.5 Gen 2.11 Gen 2.7 Gen 2.8

2nd set of fitness criteria (potential to above 2.5 m have green space) width in sq.m in percentage

3050 3439 3203 3277 2933 3797 2447 2939

37% 33% 42% 44% 45% 55% 37% 48%

1 2 3 4

Plot generation

Patch generation Mutation strategy Generation 1.1

Generation (1)

circle packing Generation 1.2

1.1

1.2

Evaluation(1) fitness(1)

fitness(2) selection

fitness(3)

Generation (1) fit patches Mutation

Generation (2)

Evaluation(2) fitness(1)

fitness(2)

selection

Generation (2)

Hierarchical Structure of Fitness criterias

12 selection

1 - Average solar radiation (wh/m²)

8

2 - Total amount of balconies (m²) - 2.5 m width balconies area/total balconies area

4 fit patches

Average solar radiation (wh/m²)

Gen 2.9 Gen 2.4 Gen 2.6 Gen 2.1 Gen 2.5 Gen 2.11 Gen 2.7 Gen 2.8 Gen 2.2 Gen 2.3 Gen 2.10 Gen 2.12

953.0 958.0 979.0 1016.0 1029.0 1030.0 1032.0 1059.0 1071.0 1102.0 1119.0 1189.0

Total amount of terraces (m²)

2291.0 2447.0 2521.0 2871.0 2933.0 2939.0 2970.0 3050.0 3203.0 3277.0 3439.0 3797.0

2.5m wide terraces area / total terraces area

32 33 37 37 42 43 44 45 45 48 51 55

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4.3 Conclusion The short-listed individuals proved to be the fittest based on the set of criterias that determined their environmental performances. Thus 2nd set with this process we develop a computational engine where various of fitness criteria Best 8 patches (potential to evaluated by 1st buildings can be clustered and their inter-relative performances can be have green space) fitness criteria studied to provide solutions forin the form. However, an aggregain percentage sq.moptimal tion strategy would be needed to scale up the patch and understand its 37% 1 3050 Gen 2.9 aggregation potential and possibilities with 33%neighbouring densities of 2 3439 Gen 2.4 42% 1would specifically deal 3 3203 Gen 2.6 different size and shapes. Thus the next chapter 44% 2 4 3277 Gen 2.1 with the aggregation and the distribution methods on an experimental 45% 3 5 2933 Gen 2.5 site. 6 Gen 2.11 55% 4 3797 7 8

Gen 2.7 Gen 2.8

2447 2939

Gen 2.1

Gen 2.5

37% 48%

Gen 2.6

Gen 2.11

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Chapter 5 - Design development...................................................................................................93 -125 5.1. Introduction 5.2. Experimental site 5.3. Design A.Seed generation B.Distribution of public spaces C.Aggregation of patches 5.4.Conclusions 5.5.Challenges and future development chapter - 5

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5.1 Introduction In the previous chapter , a generative engine was developed that could cluster buildings to develop a patch that for the given volume is appropriate in terms of its environmental performance. However,the physical distribution of these patches is critical since it needs to incorporate multiple layers of social spaces, infrastructure and also regulate movement within the neighbourhood. Thus aggregation and networking based on the vernacular abstracts would be further explored in this chapter through computational experiments. Since aggregation and network is site specific, a test site would be taken to explore the possibilities of the system since the site would provide some start conditions. The site is carefully chosen based on its current need of a climate responsive model .A part of the site would be detailed to understand the aggregation logic and addition of programmatic spaces within the patches which would ultimate set a base for a program based development for an architectural proposal on any given site.

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a

Fig 5.1 Satellite images of the site and its surrounding neighbourhoods.

[Online] Available at http://www.wikimapia.org [Accessed September 11, 2011].

b b

1.2 KM

0.5 KM

a

Fig 5.2 Satellite views of the current trends of neighbourhood design around the proposed site for experiment.

[Online] Available at http://www.wikimapia.org [Accessed September 11, 2011].

94 Al Waheda Population - 9856 Area - 1.41 km2 Density - 6990/km2 Single/two storied houses

Al Nahda 2 Population - 5000 - 6000 estd Area - 0.25 km2 Density - 20000/km2 approx 12 to 15 storied towers

Experiment site Al-Mamzar is a locality in dubai in the area of Deira, in the north east of the city. The locality is bordered by the Persian gulf to the north. It is surrounded by many recently developed neighbourhoods like Al -Waheda to the west and Al-Nahda to the east. The site is a part of Al-Mamzar and is surrounded by important landmarks like cultural centres, clubs, supermarkets, schools and mosques. The site is currently at crossroads whether to go high-rise like Al-Nahda or low-rise. Some of the general observations of the surrounding neighbourhoods are given as below a) Al Waheda - General observations i) The houses are isolated with its neighbours making the units highly vulnerable to the environmental forces. ii) Lack of hierarchy in social structure with respect to its relation of public,religious places. iii) Unshaded streets and public spaces. iv) Streets designed for vehicles than pedestrians. v) Long travel distances to points of interests due to its low dense nature

b) Al Nahda 2 - General observations i) Concentrated density results in a much compact form,resulting in large unshaded open spaces which remain unused for most part of the year and instead used for parking vehicles. ii) Comparatively shorter travel distances but the streets still remain unshaded. Lack of hierarchy in spaces and disconnected relations within programmed buildings. The patch has properties similar to â&#x20AC;&#x153; open set planning â&#x20AC;&#x153;as discussed in chapter 3.

Site General strategy for aggregation and network

Site

Pedestrian intesity (around the site) Public space

Pedestrian intesity growth points (around the site) (Wooly path system)

location

Public space growth points Expansionlocation Growth-based system (Wooly (Cellular automata) path system) (proximity to the sea)

Growth-based system (Cellular automata)

Expansion (proximity to the sea)

Overall Orientation ( based on sunlight)

Genetic Algorithm

size

Patch aggregation high to low density from the public seeds

Overall Orientation ( based on sunlight)

size

Genetic Algorithm High density patch

low density patch

Patch aggregation high to low density from the public seeds

High density patch

low density patch 95

Neighbourhood (patch aggregation)

1st step of aggregation Neighbourhood (patch aggregation) High-density patch growth from public space 1st step of aggregation boundry Set rules

Connectivity rules

- Main path

Shortest path

-Secondary path (Bike path)

Shortest path

public space to the site nodes Blocks to the node on patch

Loop making

- Tertiary path path High-density patch growth Shortest - Main path All possible paths within the patch (Pedestrian path) public space to the site nodes from public space- Touching the public boudry boundry Shortest path Loop making -Secondary path Connectivity Blocks to the node on patch (Bike path) Set rules - Being insiderules Set rules the site - Tertiary path All possible paths within the patch - Touching the the voids between (Pedestrian path) Program distribution public boudry the main road and rules patch boundry Next density patch - Being inside Set rules the site commercial the voids between Program distribution the main road and rules patch boundry Next density patch commercial

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Point of origins (buildings)

Destination (religious centres, markets,transportation nodes) center point of buildings

Lines from start to end point

pathways

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lines between 2-points

The illustration is an abstract showing the computational logic for finding the shortest routes along a given path.

A. Seed generation As abstracted from the vernacular morphologies that the public spaces are the core generators for growth around it. It was necessary to locate the public spaces at locations that would allow easy flow of pedestrians around the site into such places and meanwhile also try to make an interconnected network between the public spaces maintaining the shortest route.

For this reason, it is necessary to find the pedestrian nodes and the intensities at the nodes to figure which nodes should be directly connected. For this reason, the existing neighbourhoods around the site are modelled and the places of interests are located. Considering the site as the attractor and also the important landmarks which in this case are some schools, supermarket, sports centre and the beach, the algorithm of shortest walk provides the resultant intensities at the nodes and also the nodes which need to be connected in a way that provides a smooth flow of people through the site.

Simulation showing the shortest routes from all the buildings to the common destination. The intensity at each node is calculated while taking the shortest route. The radius of the circle represents the intensity of pedestrians.

nes: Center

enter

4 2 3

Attraction areas 1) Community center 2) Beach 3) Shopping center 4) School

1

Site map showing the points of interest around the site which needs to be connected.

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4 2 3

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Site map showing the shortest routes from all the buildings to al lthe destinations.The intensity at each node is calculated while taking the shortest route.

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Attraction field (in m)

Shortest detour paths

Direct paths between the points Points in the attraction field

The illuatration shows an abstract of the computational model used to derive the shortest route.

98 Simulations (Left) direct paths to be bundled (Top) attraction factor bundling the paths to find the locations where they would densify.

A. Seed generation Various path system experiments were done to connect the nodes , achieved in the last experiment ,to find the attraction points within the site. The attempt was to connect these nodes with the shortest detour routes and in the meanwhile develops some seeds for public spaces. A system of woolly paths was used to create attraction between the direct paths, based on the proximity to the neighbouring attraction lines , allowing the bundling of these lines.

The output of the experiment showed the shortest detour paths and their resultant intersecting nodes. However only the intersection nodes were extracted from this experiment since superimposing a fixed path network wouldnâ&#x20AC;&#x2122;t define a clear development of a bottom up system.

The nodes define the potential attraction points based on existing movement patterns around the site.

Detail of the process of attraction in which the lines are subdivided into 1m wide intervals.The attraction factor is set to a scale of 2m.Thus the points tend to meet if they are in proximity of 1m each side.

Result of the simulation showing shortest detour paths connecting the major movement nodes around the site

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nes: Center

enter

4 2 3

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The growth seeds extracted from the path experiment.

These points thus represent the potential attraction points that can serve as growth seeds.

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Main street intersections (Public spaces) as growth points Natural resources and public amenities as attractors

Total percentage of public spaces

%

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Simulation example for the public cell generation.The extent of growth in each case is pre-defined based on its proximity to closest attractor curve.

B.Public space generation

nodes as public places

The generated public spaces act as cores for development around it.

These public spaces may include religious places as well as building like markets and community centres. However, in this experiment, the public spaces are all kept as open spaces which would be further developed in the future experiments. The public spaces are thoughtfully distributed on the seeds which were achieved in the last experiment. For this process of distribution, the logic of cellular automata is used where the system grows based on states of neighbouring cells. An array of points at a distance of 1m is provided for the site, each time there is a check condition, the point checks the state of its 8 neighbours around it to finally achieve its own state. However, controlling this growth was important since it covers up the whole site .Thus a stop condition is introduced to control this growth. The idea of attractors is used to set bounds for each seed growth with predefined values of public spaces. The distance between the seed and the nearest point on attraction curve is taken and based on the distance the public spaces are distributed. The run count stops It reaches its maximum value.

Input - seed points Public space

Set array of test points

Check neighbours

Set of 1 x 1 m cells

?

If no.of neighbours(public) > 0

If the cluster more than allocated maximum value (ratio between total cells and distance of the seed from attractor)

Stop growing

Simulation examples for public space generation.

Top - simulation representing the seed points and the attractor curve.Based on the distance from curve,the public cells are distributed by the process of cellular automata.

Left- distributed public cells that stop growing once it reaches it designated value.

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nes:

y Center

Center

4 2 3

1

Results for the simulation on the site, showing multiple growth points. However only the larger spaces would be considered as attractors and others as repeller such that they would not be covered by built-forms.

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2. public spaces 10732 m3 16537 m3 20350 m3 25700 m3

The volume of patches

Aggregation Stage 1 The illustration shows the first stage of aggregation where largest densities starts growing from public spaces.

The conditions of growth are i) the cluster should have atleast one side touching the public space. ii) the cluster shouls have all boundaries within the site.

3. attraction zone

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Major Wind

N

Aggregation Stage 5 The illustration shows the last stage of aggregation where smallest densities grow touching the earlier ones. The conditions of growth are i) the cluster should have atleast one side touching any of previous density ii) the cluster should have all boundaries within the site.

C. Aggregation strategy The aggregation logic starts with the abstract of diffusing densities from the core towards the interiors of the neighbourhood. The reason behind it being, the higher density patches are complimented with bigger social and interactive spaces, thus a gradient from high to low density would create a hierarchy within the different social interactive public spaces, with bigger ones close to the public space and as we move towards the interiors, it starts reducing. Thus the rule states that, the highest density should always connects to the public space physically. This condition however encountered some cases where the patches started growing outside the site boundary. This was resolved by not allowing the densities to grow if the space is not enough for it. These conditions mark the generation of the first level of patches on the site, it is followed by other densities with similar constraints.

The diagram shows the detail growth at one of the public spaces.

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The illustration shows various densities and the combinations with different sets of built forms.

Random selection from the palette of 4 optimised options from the generative process.

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Aggregation - step 1 The first step of aggregation involves clustering of the highest density patches towards the larger attraction public spaces, with an orientation along north east to gain the most benefits of prevailing wind , higher shadow densities and also maximise sea views. The condition of aggregation is that the the public space and the patches should have atleast 1 common boundary, indicating that the density can accommodate in the given site conditions. In some instances, the public space being very close to the sea , there are few patches that cross the site boundaries. Such patches would be deleted due to site constraints. However, a smaller density might fit within the same given space .

The aggregation logic of higher density patches toward larger open spaces , makes sure that there is a second level of such spaces since larger patches have larger open areas and semi public spaces . Hence, trying to achieve a certain hierarchy in social spaces and streets.

Aggregation logic

The illustration shows the logic for the first step of aggregation where the highest density clusters aggregates with the public spaces.

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N Public spaces (Mosques/Markets/Squares)

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Aggregation - street generation

Aggregation logic

Once the first level of patches are places on site , a algorithm is developed that would find the closest way from the centre of the public space towards the surrounding nodes , with an idea of ultimately connecting all the nodes on the site with the shortest route possible after a few stages. Meanwhile , the script also generates a network of pedestrian routes within the patches considering the shortest walk towards the public space, a clear pathway is necessary such that the remainder spaces can be utilized for recreation activities and programs like cycling tracks, water bodies etc. However in the output , there are instances when the streets are not well connected at a patch scale. Thus loops are required to make the internal flow within the patch more efficient for movement from one building to the other.

The illustration shows the logic for the generation of initial phase of the street network. The system looks for the shortest route available to reach the main nodes. The system then generates shortest pedestrians routes to all the built-forms.

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N Primary roads Pedestrian streets

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Aggregation - step 2

Aggregation logic

This aggregation is continued for the next phase where newer patch-types (lesser densities) are further added in an attempt to achieve a compact model , thus trying to fit as many patches as possible with a condition that they should always connect either to public spaces or the 1st level patches.

The illustration shows the logic for the second step of aggregation where the 2nd level density clusters aggregates with the ones before it or the public spaces.

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N Primary roads

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Aggregation - street generation The pedestrian network grows along with the densities , thus getting closer to the neighbouring nodes with primary roads growing in an attempt to connect the nodes by shortest routes. Pedestrian streets are generated and is revisited to add the necessary feedback. Loops are made on the network for better connectivity for movement within the patches.

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N Primary roads

Plan

Pedestrian streets

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Aggregation - program development With the growing neighbourhood , other programs needed to be incorporated to cater the needs of the population .Thus there is a need to add program spaces like commercial buildings , markets, parking structures etc. However as abstracted from the vernacular morphologies , that such spaces are often occurring near the main streets and public spaces . Thus another rule is defined , where the system looks for empty open spaces along the primary roads . These spots are traced and based on the overall demographics of the site , the densities are distributed amongst them. Another important strategy is that of parking .

Every patch is surrounded by at least 1 primary road , thus converting a few of them into parking buildings would be efficient so that it allows free flow of people within the patches.

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N Primary roads Commercial / parking structures

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Aggregation - social spaces The next step is to evaluate the private and semi - public spaces evolved due to the aggregation process. These spaces represent the social structure of the neighbourhood. It is observed that such spaces are equally distributed amongst the considered area. A certain hierarchy is visiblefrom small courtyards to larger semi - public intervals and then to the larger public spaces .

Aggregation logic

The illustration shows the demarcation of private and semi - public spaces within the patches.

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N Semi-public spaces Private courtyards

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Site section 10 9 8 7 6 5 4 3 2 1

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Series of site sections

1

2

3 4

5

6

7 8

Sections

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Not to scale

10

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Detail sectional elevation

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5.4 Conclusion The research project, in this thesis proposes new computational, analytical and design techniques for urban development in hot-arid regions.

It shows how such techniques can be used in order to develop a bioclimatic strategy for urban development at the scale of a neighbourhood. Principles or morphological indicators, dictating the organization of cities in hot-arid regions, are extracted from the research of micro climates in vernacular morphologies. It is shown how micro climatic factors, which till date have been used only for analysis, can be used in Generative algorithms as well. Genetic algorithms in conjunction with the bio-climatic fitness criteria show how generations can be produced and evaluated simultaneously to produce optimised urban forms.

Various strategies are proposed to deal with the problems revealed in relation to computation and physical articulation of urban patches.

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5.5 Challenges and limitations The initial challenge towards the analysis of vernacular morphologies was the lack of apparent parameters that would in reality relate to the evolution of their forms. Firstly a series of micro climatic parameters were tested providing a range of mathematical values. The calculation techniques implemented were meant for organized patches with clear relative ratios, thus failing to inform the understanding. As a result they were unable to provide concrete values for evolved forms that are more organic. Nevertheless, only the parameters which are more reliable and contextual to the morphologies were selectively developed to get an idea of how urbanism can be approached in such climates.

The use of genetic algorithms at a scale of urban patches posed another challenge in terms of computational generation as well as in their analysis. The initial script with individual buildings could create various options and evaluate them simultaneously on ecotect. However as the scale was increased to a cluster, the hardware technology reached its limit.

Thus, the process was more about generating patches and then feeding them as inputs for analysis rather than it being an interactive process.

Long calculation time on ecotect added to this thus, we can say that such generative systems can be efficiently used in architectural practice only if the evaluation processes are faster.

The most time consuming process was to reassess the complex geometries, for example, refinement of meshes before it is sent to ecotect.

Whereas comparatively, the calculation time was much faster than making the geometries refined enough. Thus, better ways needs to be developed to extract and calculate data for the morphologies much faster.

Nevertheless, even if the process were time consuming, the output lead to a better understanding of the effects on various climatic indicators that are more promising and thus leading to an approach that is powerful to produce designs in the more efficient way.

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Further developments For the next phase of architectural design, further development should take place in rules for patch aggregation addressing the most important issue of orientation, which in this research was limited to a fixed orientation. A detailed investigation of this topic would make the genetic process more logical and informative, generating more variations in forms and site based optimised results.

Development should also take place at the scale of building generations. More typologies and even optimization strategy at building scale would thus generate more interesting and efficient clusters. The logics of path generation and program distribution should be revisited and further developed. The factors of randomness in mutations for patch generations should be addressed to achieve a more controlled growth.

As an architectural proposal, programs should be added to the design like agriculture, canals etc. that could efficiently utilize the existing natural resources. Also focus should be laid upon developing strategies for architectural design at building scale like openings, facades, wind

Thus as a summary, the research project presented new methodologies for designing context based urban neighbourhoods.

Such approaches respond to the urgent need of designing sustainable neighbourhoods, which along with energy efficiency should also respond to the local culture and living patterns. With the design experiments, it can be said that such micro-climatic approaches can be incorporated in the design strategies. Also at various stages of research, it is shown that even if computers can assist the design process, it is creativity of the designer and experience that should drive the design and incorporate solutions that are socially and economically viable.

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Renderings

View along a pedestrain street 122

View from the public space / square

View of a terrace of an apartment 123

View overlooking a courtyard

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Bibliography 124 Books

Articles

Brian Edwards, Magda Sibley, Mohamad Hakmi and Peter Land (2006) Courtyard housing : past, present and future Taylor and francis

Architectural Genomics

By Keith Besserud, AIA Skidmore, Owings, & Merrill LLP (BlackBox Studio) Available at - http://www.som.com/resources/documents/SOM_Architectural_Genomics.pdf

Frei Otto (2001) Occupying and Connecting Edited by Berthold Burkhardt Edward Ng (2010) Designing High-Density Cities; For social & environmental sustainability.

London: Earthscan

Arab human development report ,

By Balgis Osman Elasha Mapping of climate change threats and human development impacts in arab region Available at -www.arab-hdr.org/publications/other/ahdrps/paper02-en.pdf

Marshall Stephan (2005) Streets and Patterns Oxon: Spon Press.

Climate Sensitive Design and the Urban Morphology Integration of Morphological Parameters in Bio-Climatic Urban Planning Architectural Association Emergent Technologies & Design 2010 MArch Phase II, Thesis

Marshall Stephan (2010).

Cities design and evolution Oxon: Spon Press.

Factbox: Climate change poses threat to Middle East Published on - Sun Nov 14 2010 Available at - http://www.reuters.com/article/2010/11/14

Michael Batty (2005) , Cities and Complexity Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals MIT press

Linking Population, Poverty and Development Urbanization: A Majority in Cities Available at - http://www.unfpa.org/pds/urbanization

Peter Droege (2010) Climate design: design and planning for the age of climate change / Oro editions

Middle East: Population growth poses huge challenge for Middle East and North Africa - International Herald Tribune Published: Thursday, January 18, 2007 Available at - http://www.nytimes.com/2007/01/18/news

Rudofsky, B. (1964) Architecture without Architects London: Academy Editions.

Population reference bureau http://www.prb.org/Educators/TeachersGuides/HumanPopulation/Urbanization.

Terry Williamson, Evyatar Erell, David Pearlmutter(2010) Urban Microclimate: Designing the spaces between Buildings Earth scan / James & James

125 References 1.Marshall Stephan (2010). Cities design and evolution,pg.no 1 2.Brian Edwards, Magda Sibley, Mohamad Hakmi and Peter Land Courtyard housing : past, present and future,Pg no 141 3.Autodesk ecotect - http://usa.autodesk.com 4.Wikipedia encyclopedia- http://en.wikipedia.org/wiki/Grasshopper_3d 5.Wikipedia encyclopedia- http://en.wikipedia.org/wiki/Sudoku

Pictures Pg no. 7 - By Slocalsurfer, available at -ww.flickr.com.photoslocalsurfer.21452948 Pg no. 10 - By Sabdalla mohammad , available at -www.flickr.com.photosabdalla_mohd.4900842636 Pg.no. 34 - By Sergej Esnault , available at - www.360cities.net/search/shibam

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Appendix 127

Appendix ............................................................................................................................129-157

Shortest walk - Grasshopper / VB script

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Inputs - start and destination points

Input pathways from existing tissue

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Vb - counter for calculating the pedestrain intensity

Appendix

Generic Growth - Cluster - VB script Complete script for public space generation

Multi - state cellular automata Public cell distribution

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In the experiments only 1 state cellular automata is used until now

Public space distribution algorithm Private Sub RunScript(ByVal ATTRACTORS As List(Of Point3d), ByVal curve As Curve, ByVal point As List(Of Object), ByRef val As Object, ByRef line As Object, ByRef length As Object) Dim resi As New List(Of Double) Dim d1 As Integer = (a * b / 100) * residential Dim dist As New list(Of Double) Dim dist1 As New list(Of point3d) Dim dist2 As New list(Of line) For i As int32 = 0 To attractors.count - 1 Dim t As Double = 0 Dim da As Double = curve.ClosestPoint(attractors.item(i), t) Dim point1 As point3d = curve.PointAt(t) Dim line1 As New line(attractors.item(i), point1) dist.add(Da) dist2.add(line1) dist1.add(point1) Next Dim percentage As New List(Of Double) Dim addition As Double = 0 For i As int32=0 To dist2.count - 1 addition = addition + dist2.item(i).length Next For i As int32=0 To dist2.count - 1 percentage.Add(Math.Round(pert, 0)) Next val = percentage line = dist2 length = addition Private Sub RunScript(ByVal total As Double, ByVal perclist As List(Of Double), ByRef A As Object) Dim noofcells As New list(Of Double) For i As int32=0 To perclist.Count - 1 Dim no1 As Double = perclist.item(i) * total / 100 noofcells.add(no1) Next a = noofcells

Private Sub RunScript(ByVal p As List(Of Point3d), ByVal a As Integer, ByVal b As Integer, ByVal x As Integer, ByVal neighbours As Integer, ByVal p1 As List(Of Point3d), ByVal p2 As List(Of Point3d), ByVal p3 As List(Of Point3d), ByVal p4 As List(Of Point3d), ByRef BO As Object, ByRef C As Object, ByRef c1 As Object, ByRef q As Object, ByRef r As Object, ByRef s As Object, ByRef t As Object) bo = New Rectangle3d(plane.WorldXY, a, b) If x OrElse n = 0 Then n=0 field = initializefield(a, b, p, p1, p2, p3, p4) c2.clear c3.clear c4.clear c5.clear End If

makecells(field, c2, ncount1, c3, c4, c5) ‘make next generations field = nextgeneration(field, n, ncount1, neighbours) n += 1 print(n) q = c2 r = c3 s = c4 t = c5 End Sub ‘<Custom additional code> Dim n As Int32 = 0 Dim ncount1 As Int32 = 0 Dim field(,) As Int32 = Nothing Dim c5 As New list(Of rectangle3d) Dim c2 As New list(Of rectangle3d) Dim c3 As New list(Of rectangle3d) Dim c4 As New list(Of rectangle3d)

Appendix

131

Public Function initializefield(ByVal a As int32, ByVal b As int32, ByVal p As list(Of point3d), ByVal p1 As list(Of point3d), ByVal p2 As list(Of point3d), ByVal p3 As list(Of point3d), ByVal p4 As list(Of point3d)) As int32(,) Dim init (a,b) As Int32 For i As int32=0 To a For j As int32=0 To b init(i, j) = 0 Next Next â&#x20AC;&#x2DC;change cells with points to 1 For Each pt As point3d In p If pt.X >= 0 AndAlso pt.x <= a AndAlso pt.y >= 0 AndAlso pt.Y <= b Then init(math.Round(pt.X), math.Round(pt.Y)) = 1 Next For Each pt As point3d In p1 If pt.X >= 0 AndAlso pt.x <= a AndAlso pt.y >= 0 AndAlso pt.Y <= b Then init(math.Round(pt.X), math.Round(pt.Y)) = 1 Next For Each pt As point3d In p2 If pt.X >= 0 AndAlso pt.x <= a AndAlso pt.y >= 0 AndAlso pt.Y <= b Then init(math.Round(pt.X), math.Round(pt.Y)) = 2 Next For Each pt As point3d In p3 If pt.X >= 0 AndAlso pt.x <= a AndAlso pt.y >= 0 AndAlso pt.Y <= b Then init(math.Round(pt.X), math.Round(pt.Y)) = 3 Next 132

For Each pt As point3d In p4 If pt.X >= 0 AndAlso pt.x <= a AndAlso pt.y >= 0 AndAlso pt.Y <= b Then init(math.Round(pt.X), math.Round(pt.Y)) = 4 Next Return init End Function

Public Function makecells(ByVal field(,)As int32, ByRef c2 As List(Of rectangle3d), ByVal ncount1 As int32, ByRef c3 As List(Of rectangle3d), ByRef c4 As List(Of rectangle3d), ByRef c5 As List(Of rectangle3d)) As Boolean Dim a As int32 = ubound(field, 1) Dim b As int32 = ubound(field, 2) Dim circle As New List (Of circle) Dim circle_a As New List(Of rectangle3d) Dim circle_b As New List(Of rectangle3d) Dim circle_c As New List(Of rectangle3d) Dim circle_d As New List(Of rectangle3d) For i As int32= 0 To a For j As int32 = 0 To b Dim cr1 As New Point3d(i + 0.5, j + 0.5, 0) Dim cr2 As New Point3d(i - 0.5, j - 0.5, 0) If field(i, j) = 1 Then Dim mycircle As New rectangle3d(plane.WorldXY, cr1, cr2) circle_b.add(mycircle) ElseIf field(i, j) = 2 Then Dim mycircle2 As New rectangle3d(plane.WorldXY, cr1, cr2) circle_c.add(mycircle2) ncount1 += 1 ElseIf field(i, j) = 3 Then Dim mycircle3 As New rectangle3d(plane.WorldXY, cr1, cr2) circle_d.add(mycircle3) End If Next Next c2 = circle_a c3 = circle_b c4 = circle_c c5 = circle_d Return True End Function

Public Function nextgeneration(ByVal field(,)As int32, ByVal n As int32, ByVal ncount1 As int32, ByVal neighbours As Integer) As int32(,) Dim a As int32 = ubound(field, 1) Dim b As int32 = ubound(field, 2) Dim nc As int32 = ncount1 Dim nextgen (a,b) As int32 For i As int32= 0 To a For j As int32= 0 To b Dim n1 As Int32 = countneighbours(i, field, j, 1) Dim n2 As Int32 = countneighbours(i, field, j, 2) Dim n3 As Int32 = countneighbours(i, field, j, 3) Dim n4 As Int32 = countneighbours(i, field, j, 4) If field(i, j) = 0 Then If field(i, j) = 1 Then nextgen(i, j) = 1 End If If n1 > neighbours Then nextgen(i, j) = 2 End If Else nextgen(i, j) = 0 End If If field(i, j) = 1 Then nextgen(i, j) = 1 End If If nc < 35 Then If field(i, j) = 1 Then nextgen(i, j) = 1 End If If field(i, j) = 2 Then nextgen(i, j) = 2 End If If field(i, j) = 0 Then If n2 > 0 Then nextgen(i, j) = 3 End If Else nextgen(i, j) = 0 End If If field(i, j) = 3 Then nextgen(i, j) = 3 End If End If Next Next field = nextgen Return field End Function

Public Function countneighbours(ByVal i As int32, ByVal field (,)As int32, ByVal j As int32, ByVal value As int32) As int32 Dim a As int32 = ubound(field, 1) Dim b As int32 = ubound(field, 2) Dim nh As int32 = 0 â&#x20AC;&#x2DC;value range 0 to 3 If i - 1 >= 0 Then If field(i - 1, j) = value Then nh += 1 End If End If If i + 1 <= a Then If field(i + 1, j) = value Then nh += 1 End If End If If j - 1 >= 0 Then If field(i, j - 1) = value Then nh += 1 End If End If If j + 1 <= b Then If field(i, j + 1) = value Then nh += 1 End If End If If ((i - 1 >= 0) AndAlso (j - 1 >= 0)) Then If field(i - 1, j - 1) = value Then nh += 1 End If End If If ((i + 1 <= a) AndAlso (j + 1 <= b)) Then If field(i + 1, j + 1) = value Then nh += 1 End If End If If ((i + 1 <= a) AndAlso (j - 1 >= 0)) Then If field(i + 1, j - 1) = value Then nh += 1 End If End If If ((i - 1 >= 0) AndAlso (j + 1 <= b)) Then If field(i - 1, j + 1) = value Then nh += 1 End If End If Return nh

133

End Function

Appendix

Generic Growth - Cluster - VB script

Private Sub RunScript(ByVal x As Double, ByVal y As Double, ByVal z As Double, ByRef A As Object, ByRef B As Object, ByRef C As Object, ByRef D As Object, ByRef E As Object, ByRef F As Object)

134

Dim v As New List(Of Point3d) Dim dividline As New List(Of Line) Dim v1 As New List(Of Point3d) Dim v2 As New List(Of Point3d) Dim lp As New List(Of Polyline) Dim vec As New Vector3d(0, 0, z) ‘Dim Lsur As New List(Of Surface) Dim Lbrep As New list(Of Brep) ‘surface.CreateExtrusion ‘brep. ‘brep.CreateBooleanDifference( ‘dim srf as surface = surface.CreateExtrusion( Dim bigbox As New List(Of Point3d) Dim aa As Double = math.Sqrt(x) Dim p1 As New point3d(-aa / 2, -aa / 2, 0) bigbox.Add(p1) Dim p2 As New Point3d(aa / 2, -aa / 2, 0) bigbox.Add(p2) Dim p3 As New Point3d(aa / 2, aa / 2, 0) bigbox.Add(p3) Dim p4 As New Point3d(-aa / 2, aa / 2, 0) bigbox.Add(p4) bigbox.Add(p1) Dim poly1 As New polyline(bigbox) ‘Dim np As New point3d(0, 0, 0) ‘Dim poly2 As New Polyline(poly1) ‘Dim sc As Transform = Transform.Scale(np, 1.15) ‘poly2.Transform(sc) Dim scale As New List(Of Point3d) Dim ss1 As New point3d((-aa / 2 - 2.5), (-aa / 2 - 2.5), 0) scale.Add(ss1) Dim ss2 As New Point3d((aa / 2 + 2.5), (-aa / 2 - 2.5), 0) scale.Add(ss2) Dim ss3 As New Point3d((aa / 2 + 2.5), (aa / 2 + 2.5), 0) scale.Add(ss3) Dim ss4 As New Point3d((-aa / 2 - 2.5), (aa / 2 + 2.5), 0) scale.Add(ss4) scale.Add(ss1) Dim poly2 As New polyline(scale)

Dim prop As areamassproperties = areamassproperties.

Compute(poly2.ToNurbsCurve) Dim area2 As Double = prop.Area ‘d = poly1 d = area2 ‘f = poly2 ‘courtyard Dim yy As Double = (math.Sqrt(x * y)) / 2 ‘ c = yy Dim pt11 As New Point3d(-yy, -yy, 0) Dim pt22 As New point3d(yy, -yy, 0) Dim pt33 As New Point3d(yy, yy, 0) Dim pt44 As New point3d (-yy, yy, 0) Dim pt1 As New Point3d(p1.X, pt11.Y, 0) ‘= poly1.PointAt(y) Dim pt2 As New point3d(pt22.X, p2.Y, 0) ‘= poly1.PointAt(z) Dim pt3 As New Point3d(pt22.X, p2.Y, 0) ‘= poly1.PointAt(1 + y) Dim pt4 As New point3d(pt33.X, p2.Y, 0) ‘= poly1.PointAt(1 + z) Dim pt5 As New Point3d(pt22.X, p3.Y, 0) ‘= poly1.PointAt(2 + y) Dim pt6 As New point3d(pt11.X, p3.Y, 0) ‘= poly1.PointAt(2 + z) Dim pt7 As New Point3d(pt33.x, p1.y, 0) ‘ = poly1.PointAt(3 + y) Dim pt8 As New point3d(pt11.X, p1.Y, 0) ‘= poly1.PointAt(3 + z) v.Add(pt1) v.Add(pt2) v.Add(pt3) v.Add(pt4) v.Add(pt5) v.Add(pt6) v.Add(pt7) v.Add(pt8) Dim l As New List(Of Point3d) l.Add(pt11) l.Add(pt22) l.Add(pt33) l.Add(pt44) l.Add(pt11) Dim built As New Polyline(bigbox) Dim s1 As Surface = surface.CreateExtrusion(built.ToNurbsCurve, vec * 1.5) Dim br As Brep = s1.ToBrep br = br.CapPlanarHoles(0.1) ‘1 moraba If x < 150 Then Lbrep.add(br) End If ‘2 moraba ba haiat If x >= 150 AndAlso x < 225 Then Dim lpp As New List(Of Polyline) ‘ Dim built As New Polyline(bigbox) Dim courthyard As New Polyline(l) Dim arr1 As New List(Of brep) arr1.Add(br) Dim srf As Surface = surface.CreateExtrusion(courthyard.ToNurbsCurve, vec * 1.5) br = srf.ToBrep br = br.CapPlanarHoles(.1) Dim arr2 As New List(Of brep) arr2.Add(br) Dim brrr() As Brep = Brep.CreateBooleanDifference(arr1, arr2, 0.01) Lbrep.add(brrr(0)) End If

v1.Add(pt5) v1.Add(pt33) v1.Add(pt44) v1.Add(pt8) v1.Add(p1) v1.Add(p4) v1.Add(pt5) a = pt8 v2.Add(pt5) v2.Add(p3) v2.Add(p2) v2.Add(pt8) v2.Add(pt11) v2.Add(pt22) v2.Add(pt5) ‘ 3 L shape If x >= 225 AndAlso x < 400 Then Dim lpp As New List(Of Polyline) Dim pl As New Polyline(v1) ‘a = v1 Dim td As Transform = Transform.Translation(poly2.item(3) pl.item(5)) pl.Transform(td) Dim pll As New Polyline(v2) Dim ttd As Transform = Transform.Translation(poly2.item(1) - pll. item(2)) pll.Transform(ttd) lpp.Add(pl) lpp.Add(pll) If x >= 225 AndAlso x < 325 Then For i As int32 = 0 To lpp.Count - 1 Dim s2 As Surface = surface.CreateExtrusion(lpp.item(i).ToNurbsCurve, vec * 3) Dim br2 As Brep = s2.ToBrep br2 = br2.CapPlanarHoles(0.1) Lbrep.add(br2) Next End If

1))

Dim mos As New List(Of rectangle3d) Dim moss As New List(Of polyline) Dim rec0 As New Rectangle3d(plane.WorldXY, p1, pt22) Dim rec1 As New Rectangle3d(plane.WorldXY, p2, pt33) Dim rec2 As New Rectangle3d(plane.WorldXY, p3, pt44) Dim rec3 As New Rectangle3d(plane.WorldXY, p4, pt11) mos.Add(rec0) mos.Add(rec1) mos.Add(rec2) mos.Add(rec3) For i As int32 = 0 To mos.Count - 1 Dim recc As New Polyline recc = mos.item(i).ToPolyline moss.add(recc) Next For i As int32 = 0 To moss.Count - 1 Dim t As New Transform t = Transform.Translation(poly2.item(i + 1) - moss.item(i).item(i +

moss.item(i).Transform(t) Next Dim tt As New Transform tt = Transform.Translation((moss.item(0).item(2)) - (moss.item(1). item(1))) moss.item(1).Transform(tt) Dim ttt As New Transform ttt = Transform.Translation((moss.item(2).item(0)) - (moss.item(3).

moss.item(3).Transform(ttt) If x >= 325 AndAlso x < 400 Then For i As int32 = 0 To moss.Count - 1 Dim s As Surface = surface.CreateExtrusion(moss.item(i).ToNurbsCurve, vec * (i + 2)) Dim rec As Brep = s.ToBrep rec = rec.CapPlanarHoles(0.1) Lbrep.add(rec) Next End If End If ‘ 4 4ta moraba If x >= 400 AndAlso x < 900 Then Dim lppp As New list (Of polyline) Dim subtract As New list (Of Rectangle3d) Dim rec1 As New Rectangle3d(plane.WorldXY, pt44, p3) Dim pl1 As Polyline = rec1.ToPolyline Dim t As Transform = Transform.Translation(poly2.item(2) - pl1. item(2)) pl1.Transform(t) lp.Add(pl1) lppp.add(pl1) Dim sub1 As New Rectangle3d(plane.WorldXY, pt33, p3) sub1.Transform(t) subtract.add(sub1) Dim rec2 As New Rectangle3d(plane.WorldXY, pt33, p2) Dim pl2 As Polyline = rec2.ToPolyline Dim tt As Transform = Transform.Translation(poly2.item(1) - pl2. item(1)) pl2.Transform(tt) lp.Add(pl2) lppp.add(pl2) Dim sub2 As New Rectangle3d(plane.WorldXY, pt22, p2) sub2.Transform(tt) subtract.add(sub2) Dim rec3 As New Rectangle3d(plane.WorldXY, pt22, p1) Dim pl3 As Polyline = rec3.ToPolyline Dim ttt As Transform = Transform.Translation(poly2.item(0) - pl3. item(0)) pl3.Transform(ttt) lp.Add(pl3) lppp.add(pl3) Dim sub3 As New Rectangle3d(plane.WorldXY, pt11, p1) sub3.Transform(ttt) subtract.add(sub3) Dim rec4 As New Rectangle3d(plane.WorldXY, pt11, p4) Dim pl4 As Polyline = rec4.ToPolyline Dim tttt As Transform = Transform.Translation(poly2.item(3) - pl4. item(3)) pl4.Transform(tttt) lp.Add(pl4) lppp.add(pl4) Dim sub4 As New Rectangle3d(plane.WorldXY, pt44, p4) sub4.Transform(tttt) subtract.add(sub4) rec1.PointAt(0.5) Dim ex As New List(Of Brep) For i As int32 = 0 To subtract.count - 1 Dim g As Surface = surface.CreateExtrusion(subtract.item(i).ToNurbsCurve, (-vec * (i + 2)) / 1.8) Dim gg As Brep = g.ToBrep Appendix

135

Generic Growth - Cluster - VB script If x >= 400 AndAlso x < 700 Then â&#x20AC;&#x2DC;Dim exx As New List(Of Brep) For i As int32 = 0 To lppp.Count - 1 Dim s3 As Surface = surface.CreateExtrusion(lppp.item(i).

ToNurbsCurve, vec * 4) Dim br3 As Brep = s3.ToBrep br3 = br3.CapPlanarHoles(0.1) Lbrep.add(br3) Next End If If x >= 700 AndAlso x < 900 Then For i As int32 = 0 To lppp.Count - 1 Dim s33 As Surface = surface.CreateExtrusion(lppp.item(i).

ToNurbsCurve, (vec * (i + 3))) Dim br33 As Brep = s33.ToBrep br33 = br33.CapPlanarHoles(0.1) Lbrep.add(br33) Next End If End If

136

Dim zz As Double = (math.Sqrt(900 * y)) / 2 Dim fix As New List(Of Point3d) Dim fixp1 As New point3d(-zz, -zz, 0) Dim fixp2 As New point3d(zz, -zz, 0) Dim fixp3 As New point3d(zz, zz, 0) Dim fixp4 As New point3d(-zz, zz, 0) fix.Add(fixp1) fix.Add(fixp2) fix.Add(fixp3) fix.Add(fixp4) If x >= 900 AndAlso x <= 2025 Then Dim lpp As New list(Of Polyline) Dim rec1 As New Rectangle3d(plane.WorldXY, fixp4, p3) Dim pl1 As Polyline = rec1.ToPolyline Dim t As Transform = Transform.Translation(poly2.item(2) - pl1. item(2)) pl1.Transform(t) lpp.Add(pl1) Dim rec2 As New Rectangle3d(plane.WorldXY, fixp3, p2) Dim pl2 As Polyline = rec2.ToPolyline Dim tt As Transform = Transform.Translation(poly2.item(1) - pl2. item(1)) pl2.Transform(tt) lpp.Add(pl2)

Dim rec3 As New Rectangle3d(plane.WorldXY, fixp2, p1) Dim pl3 As Polyline = rec3.ToPolyline Dim ttt As Transform = Transform.Translation(poly2.item(0) - pl3. item(0)) pl3.Transform(ttt) lpp.Add(pl3) Dim rec4 As New Rectangle3d(plane.WorldXY, fixp1, p4) Dim pl4 As Polyline = rec4.ToPolyline Dim tttt As Transform = Transform.Translation(poly2.item(3) - pl4. item(3)) pl4.Transform(tttt) lpp.Add(pl4) rec1.PointAt(0.5) Dim ll As New List(Of List(Of Point3d)) Dim v111 As New List(Of List(Of Point3d)) Dim v222 As New List(Of List(Of Point3d)) Dim v333 As New List(Of List(Of Point3d)) For i As int32 = 0 To lpp.Count - 1 Dim ll1 As New List(Of Point3d) Dim v11 As New List(Of Point3d) Dim v22 As New List(Of Point3d) Dim v33 As New List(Of Point3d) Dim ppt1 As Point3d = lpp.item(i).PointAt(0.35) Dim ppt2 As point3d = lpp.item(i).PointAt(0.65) Dim ppt3 As Point3d = lpp.item(i).PointAt(1.36) Dim ppt4 As point3d = lpp.item(i).PointAt(1.64) Dim ppt5 As Point3d = lpp.item(i).PointAt(2.35) Dim ppt6 As point3d = lpp.item(i).PointAt(2.65) Dim ppt7 As Point3d = lpp.item(i).PointAt(3.36) Dim ppt8 As point3d = lpp.item(i).PointAt(3.64) Dim ppt11 As New Point3d(ppt1.X, ppt8.Y, 0) Dim ppt22 As New point3d(ppt2.X, ppt8.Y, 0) Dim ppt33 As New Point3d(ppt2.X, ppt4.Y, 0) Dim ppt44 As New point3d (ppt1.X, ppt4.Y, 0) Dim p01 As Point3d = lpp.item(i).PointAt(0) Dim p02 As point3d = lpp.item(i).PointAt(1) Dim p03 As Point3d = lpp.item(i).PointAt(2) Dim p04 As point3d = lpp.item(i).PointAt(3) ll1.Add(ppt11) ll1.Add(ppt22) ll1.Add(ppt33) ll1.Add(ppt44) ll1.Add(ppt11) ll.Add(ll1) v11.Add(ppt6) v11.Add(p03) v11.Add(p02) v11.Add(ppt2) v11.Add(ppt33) v11.Add(ppt44) v11.Add(ppt6) v22.Add(ppt6) v22.Add(p04) v22.Add(p01) v22.Add(ppt2) v22.Add(ppt22) v22.Add(ppt11) v22.Add(ppt6) v33.Add(p01)

item(3))) moss.item(3).Transform(ttt) If x >= 325 AndAlso x < 400 Then For i As int32 = 0 To moss.Count - 1 Dim s As Surface = surface.CreateExtrusion(moss.item(i).ToNurbsCurve, vec * (i + 2)) Dim rec As Brep = s.ToBrep rec = rec.CapPlanarHoles(0.1) Lbrep.add(rec) Next End If v33.Add(p03) v33.Add(p04) v33.Add(p01) v111.Add(v11) v222.Add(v22) v333.Add(v33) Next â&#x20AC;&#x2DC; 5 moraba ba haiat If x >= 900 AndAlso x < 1225 Then b = lpp Dim lppp As New List(Of Polyline) Dim br5 As New List(Of Brep) Dim br6 As New List(Of Brep) For i As int32 = 0 To lpp.Count - 1 Dim courthyard As New Polyline(ll.Item(i)) lppp.Add(courthyard) Next If x >= 900 AndAlso x < 1025 Then For i As int32 = 0 To lpp.Count - 1 Dim s3 As Surface = surface.CreateExtrusion(lpp.item(i).ToNurbsCurve, vec * 5) Dim br3 As Brep = s3.ToBrep br3 = br3.CapPlanarHoles(0.1) br5.add(br3) Next For i As int32 = 0 To lppp.Count - 1 Dim s4 As Surface = surface.CreateExtrusion(lppp.item(i).ToNurbsCurve, vec * 5) Dim br4 As Brep = s4.ToBrep br4 = br4.CapPlanarHoles(0.1) br6.add(br4) Next Dim br7() As Brep = Brep.CreateBooleanDifference(br5, br6, 0.1) Lbrep.addrange(br7) End If If x >= 1025 AndAlso x < 1225 Then For i As int32 = 0 To lpp.Count - 1 Dim s3 As Surface = surface.CreateExtrusion(lpp.item(i).ToNurbsCurve, ((vec * 1.2) * (i + 2))) Dim br3 As Brep = s3.ToBrep br3 = br3.CapPlanarHoles(0.1) br5.add(br3) Next For i As int32 = 0 To lppp.Count - 1 Dim s4 As Surface = surface.CreateExtrusion(lppp.item(i).ToNurbsCurve, ((vec * 1.2) * (i + 2))) Dim br4 As Brep = s4.ToBrep br4 = br4.CapPlanarHoles(0.1)

Dim br7() As Brep = Brep.CreateBooleanDifference(br5, br6, 0.1) Lbrep.addrange(br7) End If If x >= 1025 AndAlso x < 1225 Then For i As int32 = 0 To lpp.Count - 1 Dim s3 As Surface = surface.CreateExtrusion(lpp.item(i).ToNurbsCurve, ((vec * 1.2) * (i + 2))) Dim br3 As Brep = s3.ToBrep br3 = br3.CapPlanarHoles(0.1) br5.add(br3) Next For i As int32 = 0 To lppp.Count - 1 Dim s4 As Surface = surface.CreateExtrusion(lppp.item(i).ToNurbsCurve, ((vec * 1.2) * (i + 2))) Dim br4 As Brep = s4.ToBrep br4 = br4.CapPlanarHoles(0.1) br6.add(br4) Next Dim br7() As Brep = Brep.CreateBooleanDifference(br5, br6, 0.1) Lbrep.addrange(br7) End If End If If x >= 1225 AndAlso x < 1600 Then Dim lppp As New list(Of Polyline) Dim lppp1 As New list(Of Polyline) For i As int32 = 0 To lpp.Count - 1 Dim r As New Polyline(v111.item(i)) Dim r2 As New Polyline(r) 137

If i > (lpp.count - 1) / 2 Then Dim oo As Transform = Transform.Translation(1.75, 0, 0) r2.Transform(oo) End If Dim rr As New Polyline(v222.item(i)) Dim rr2 As New Polyline(rr) If i < 2 Then Dim oo2 As Transform = Transform.Translation(-1.75, 0, 0) rr2.Transform(oo2) End If lppp.Add(r2) lppp.Add(rr2) Next lppp1.add(lppp.item(1)) lppp1.add(lppp.item(3)) lppp1.add(lppp.item(4)) lppp1.add(lppp.item(6)) lppp.Remove(lppp.item(1)) lppp.remove(lppp.item(2)) lppp.remove(lppp.item(2)) lppp.remove(lppp.item(3)) If x >= 1225 AndAlso x < 1325 Then For i As int32 = 0 To lppp.Count - 1 Dim s2 As Surface = surface.CreateExtrusion(lppp.item(i).ToNurbsCurve, vec * 4.5) Dim br2 As Brep = s2.ToBrep br2 = br2.CapPlanarHoles(0.1) Lbrep.add(br2) Next Appendix

Generic Growth - Cluster - VB script

lpppp.Add(pll1)

Dim s3 As Surface = surface.CreateExtrusion(lppp1.item(i).ToNurbsCurve, vec * 4.5) Dim br3 As Brep = s3.ToBrep br3 = br3.CapPlanarHoles(0.1) Lbrep.add(br3) Next Else For i As int32 = 0 To lppp.Count - 1 Dim s2 As Surface = surface.CreateExtrusion(lppp.item(i).ToNurbsCurve, vec * 4.5) Dim br2 As Brep = s2.ToBrep br2 = br2.CapPlanarHoles(0.1) Lbrep.add(br2) Next For i As int32 = 0 To lppp1.Count - 1 Dim s3 As Surface = surface.CreateExtrusion(lppp1.item(i).

ToNurbsCurve, vec * 4.5) Dim br3 As Brep = s3.ToBrep br3 = br3.CapPlanarHoles(0.1) Lbrep.add(br3) Next End If End If

Dim rec22 As New Rectangle3d(plane.WorldXY, v333.item(i). item(1), ll.item(i).item(2)) Dim pll2 As Polyline = rec22.ToPolyline If i >= 2 Then Dim oo2 As Transform = Transform.Translation(1.5, 0, 0) pll2.Transform(oo2) End If If i = 3 Then Dim ooo22 As Transform = Transform.Translation(0, -1.5, 0) pll2.transform(ooo22) End If If i = 0 Then Dim ooo222 As Transform = Transform.Translation(0, -1.5, 0) pll2.transform(ooo222) End If lpppp.Add(pll2)

If x >= 1600 AndAlso x <= 2025 Then Dim lpppp As New List(Of Polyline) Dim lpppp1 As New list(Of polyline) 138

For i As int32 = 0 To lpp.Count - 1 Dim rec11 As New Rectangle3d(plane.WorldXY, v333.item(i). item(0), ll.item(i).item(1)) Dim pll1 As Polyline = rec11.ToPolyline If i < 2 Then Dim oo As Transform = Transform.Translation(-1.5, 0, 0) pll1.Transform(oo) End If If i = 0 Then Dim ooo1 As Transform = Transform.Translation(0, -1.5, 0) pll1.transform(ooo1) End If If i = 3 Then Dim ooo11 As Transform = Transform.Translation(0, -1.5, 0) pll1.transform(ooo11) End If

Dim rec33 As New Rectangle3d(plane.WorldXY, v333.item(i). item(2), ll.item(i).item(3)) Dim pll3 As Polyline = rec33.ToPolyline If i >= 2 Then Dim oo4 As Transform = Transform.Translation(1.5, 0, 0) pll3.Transform(oo4) End If If i = 2 Then Dim ooo3 As Transform = Transform.Translation(0, 1.5, 0) pll3.transform(ooo3) End If If i = 1 Then Dim ooo333 As Transform = Transform.Translation(0, 1.5, 0) pll3.transform(ooo333) End If lpppp.Add(pll3) Dim rec44 As New Rectangle3d(plane.WorldXY, v333.item(i). item(3), ll.item(i).item(0)) Dim pll4 As Polyline = rec44.ToPolyline If i < 2 Then Dim oo1 As Transform = Transform.Translation(-1.5, 0, 0) pll4.Transform(oo1) End If If i = 1 Then Dim ooo2 As Transform = Transform.Translation(0, 1.5, 0) pll4.transform(ooo2) End If If i = 2 Then Dim ooo22 As Transform = Transform.Translation(0, 1.5, 0) pll4.transform(ooo22) End If lpppp.Add(pll4) rec11.PointAt(0.5) Next lpppp1.add(lpppp.item(0)) lpppp1.add(lpppp.item(7)) lpppp1.add(lpppp.item(10)) lpppp1.add(lpppp.item(13)) lpppp.Remove(lpppp.item(0)) lpppp.remove(lpppp.item(6)) lpppp.remove(lpppp.item(8)) lpppp.remove(lpppp.item(10))

For i As int32 = 0 To lpppp.Count - 1 Dim s3 As Surface = surface.CreateExtrusion(lpppp.item(i).ToNurbsCurve, vec * 3.5) Dim br3 As Brep = s3.ToBrep br3 = br3.CapPlanarHoles(0.1) Lbrep.add(br3) Next For i As int32 = 0 To lpppp1.Count - 1 Dim s4 As Surface = surface.CreateExtrusion(lpppp1.item(i).

ToNurbsCurve, vec * 5) Dim br4 As Brep = s4.ToBrep br4 = br4.CapPlanarHoles(0.1) Lbrep.add(br4) Next End If End If A = lp C = Lbrep End Sub

139

Appendix

Genetic algorithm - Patch generation

140

141

Appendix

Genetic algorithm- Patch generation- VB Script/Grasshopper

142

143

Appendix

Mutation 3 - Slab Movement - Grasshopper script

144

Average Daily Solar Radiation - Grasshopper/geco script

145

Appendix

Environmental analysis Average Daily Solar Radiation :Generation 1

1a.1

1a.4

Total Solar Radiation Total Average 1 46446205

1115

Roof Top Total SolRad (Wh) 23114416

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 3978

23174008

facade Average SolRad (Wh/m2) 786

1a.2

Total Solar Radiation Total Average 4 45732938

1109

Roof Top Total SolRad (Wh) 21009944

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4028

24056008

facade Average SolRad (Wh/m2) 812

1a.5

146

Total Solar Radiation Total Average 2 47484164

1161

Roof Top Total SolRad (Wh) 22218054

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4135

24780080

facade Average SolRad (Wh/m2) 845

1a.3

5 43446980

1104

Roof Top Total SolRad (Wh) 18852266

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4014

24126086

facade Average SolRad (Wh/m2) 828

1a.6

Total Solar Radiation Total Average 3 42539431

Total Solar Radiation Total Average

1219

Roof Top Total SolRad (Wh) 20583491

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4050

21054120

facade Average SolRad (Wh/m2) 883

Total Solar Radiation Total Average 6 48074265

1101

Roof Top Total SolRad (Wh) 26671737

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 3795

21335851

facade Average SolRad (Wh/m2) 721

1a.7

1a.10

Total Solar Radiation Total Average 7 42300068

1166

Roof Top Total SolRad (Wh) 24279366

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 3962

17553856

facade Average SolRad (Wh/m2) 731

1a.8

Total Solar Radiation Total Average 10 47681300

1129

Roof Top Total SolRad (Wh) 23856748

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 3970

22957825

facade Average SolRad (Wh/m2) 783

1a.11

147

Total Solar Radiation Total Average 8 47158068

1131

Roof Top Total SolRad (Wh) 21810981

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 3915

24393933

facade Average SolRad (Wh/m2) 825

1a.9

11 46706672

1119

Roof Top Total SolRad (Wh) 21587753

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 3920

24985971

facade Average SolRad (Wh/m2) 837

1a.12

Total Solar Radiation Total Average 9 46959688

Total Solar Radiation Total Average

1120

Roof Top Total SolRad (Wh) 21909018

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 3993

24140011

facade Average SolRad (Wh/m2) 816

Total Solar Radiation Total Average 12 48991471

1203

Roof Top Total SolRad (Wh) 23842403

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4010

24526544

facade Average SolRad (Wh/m2) 878

Appendix

Insolation Analaysis :Generation 1a

1a.1

1a.4

1a.2

1a.5

1a.3

1a.6

148

1a.7

1a.10

1a.8

1a.11

149

1a.9

1a.12

Appendix

Average Daily Solar Radiation :Generation 1b

1b.1

1b.4

Total Solar Radiation Total Average 1 43638253

1230

Roof Top Total SolRad (Wh) 26088177

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4019

17745881

facade Average SolRad (Wh/m2) 789

1b.2

Total Solar Radiation Total Average 4 50028409

1417

Roof Top Total SolRad (Wh) 29443345

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4245

20297481

facade Average SolRad (Wh/m2) 975

1b.5

150

Total Solar Radiation Total Average 2 49861050

1344

Roof Top Total SolRad (Wh) 24515106

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4065

26981205

facade Average SolRad (Wh/m2) 1102

1b.3

5 50783510

1389

Roof Top Total SolRad (Wh) 25362845

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4177

25643988

facade Average SolRad (Wh/m2) 1120

1b.6

Total Solar Radiation Total Average 3 42566413

Total Solar Radiation Total Average

1270

Roof Top Total SolRad (Wh) 23131175

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4032

19033505

facade Average SolRad (Wh/m2) 864

Total Solar Radiation Total Average 6 49830169

1440

Roof Top Total SolRad (Wh) 27165456

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4039

22237415

facade Average SolRad (Wh/m2) 1095

1b.7

1b.10

Total Solar Radiation Total Average 7 50916104

1404

Roof Top Total SolRad (Wh) 25745326

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4270

24629371

facade Average SolRad (Wh/m2) 1088

1b.8

Total Solar Radiation Total Average 10 49335516

1343

Roof Top Total SolRad (Wh) 29029510

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 3988

19943114

facade Average SolRad (Wh/m2) 899

1b.11

151

Total Solar Radiation Total Average 8 50971147

1428

Roof Top Total SolRad (Wh) 29258546

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4289

21203808

facade Average SolRad (Wh/m2) 996

1b.9

11 47457705

1370

Roof Top Total SolRad (Wh) 27069909

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4160

20256172

facade Average SolRad (Wh/m2) 943

1b.12

Total Solar Radiation Total Average 9 47780288

Total Solar Radiation Total Average

1315

Roof Top Total SolRad (Wh) 28592100

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4099

18518504

facade Average SolRad (Wh/m2) 827

Total Solar Radiation Total Average 12 51059666

1413

Roof Top Total SolRad (Wh) 26936384

Roof Top facade Total Average SolRad (Wh) SolRad (Wh) 4217

24317129

facade Average SolRad (Wh/m2) 1100

Appendix

Insolation Analaysis :Generation 1b

1b.1

1b.4

1b.2

1b.5

1b.3

1b.6

152

1b.7

1b.10

1b.8

1b.11

153

1b.9

1b.12

Appendix

Average Daily Solar Radiation :Generation 2

2.1

2.4

Total Solar Radiation Total Average 2.1.10

49770072

1016

2.2

Total Solar Radiation Total Average 2.4.8

46634877

1039

2.5

154

Total Solar Radiation Total Average 2.2.7

49261280

1071

2.3

Total Solar Radiation Total Average 2.5.6

1029

2.6

Total Solar Radiation Total Average

Total Solar Radiation Total Average 2.3.9

50960598

47061696

1102

2.6.1

49031200

979

2.7

2.10

Total Solar Radiation Total Average

Total Solar Radiation Total Average 2.7.12

50262657

1032

2.8

2.10.2

44065513

1119

2.11

155

Total Solar Radiation Total Average 2.8.2

49923868

1059

2.9

Total Solar Radiation Total Average 2.11.4

1030

2.12

Total Solar Radiation Total Average

Total Solar Radiation Total Average 2.9.11

48525839

48726187

953

2.12.9

51056020

1189

Appendix