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OPTIMA

OPTIMA

GA-infused architectural design process for a high-rise building in Sydney

Alessandro Cascone Alessandro Cascone


OPTIMA

GA-infused architectural design process for a high-rise building in Sydney

TESI DI LAUREA IN ARCHITETTURA E COMPOSIZIONE ARCHITETTONICA III CORSO DI LAUREA IN INGEGNERIA EDILE - ARCHITETTURA SCUOLA DI INGEGNERIA E ARCHITETTURA ALMA MATER STUDIORUM - UNIVERSITÀ DI BOLOGNA AA. 2016-2017 RELATORE: PROF. ALESSIO ERIOLI CO - RELATORE: PROF. PAUL WINTOUR CANDIDATO: ALESSANDRO CASCONE


ABSTRACT

Concepts such as complexity, non-linearity and continuous differentiation have replaced those of modern thinking such as determinism, linearity and serial repetition. At the same time as these changes, cities are also changing form and reason for being. In fact, according to UN-Habitat over the next 20 years, more than two billion people will enrich the population of cities. One of the ways to accommodate this new and intense flow of people is to densify even more the urban fabric. The potential risk is to transform cities and buildings as saleable products through the global market, hence, confining architectural concepts such as housing quality and aesthetics to some sort of luxury optional. The complexity and unpredictability of contemporary and future urban and architectural fabric can best be deciphered by the new design approach that has emerged in recent years. The recent relationship developed between morphogenetic, technological and computational tools, and scientific and philosophical developments has led to the emergence of a new architectural thought and a new aesthetic sensibility able to better respond, compared to the postmodernism, to the demands of the complex world in to which we live. Starting from these assumptions, the purpose of this work is to design a mixed-use skyscraper in a rapidly expanding city like Sydney in order to respond to the urgent needs of the new contemporary city. By conceiving the architectural space as an organism capable

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ABSTRACT

of evolving and adapting to the stimuli deriving from the ecosystem, there will be applied concepts of variation, mutation, inheritance and selection specific of the evolutionary theories. The ultimate goal is to obtain an architectural product from an evolutionary process that is capable of adapting to the contingencies of the surrounding environment. The design will be based on the implementation of Genetic Algorithms (GAs) able to simulate the evolutionary process of species in biology by mapping specific data of a system into strings capable of ranking the system based on specific fitness values. These algorithms are a powerful tool in finding optimal local states, but they needs a clear and intelligent definition of fitness functions to achieve a valid result set. The GAs will be used to solve a multi-objective optimization problem (MOO) using the solar access and structural performance of the building as fitness functions, then selecting an efficient Pareto option. Furthermore, it will develop a structural system able to selforganize itself based on a finite element analysis (FEM) performed on the entire volume of the building. In conclusion, a skin system congruent with the structural one will be developed reacting to external environmental stimuli. The result of this approach will not only have a pragmatic - functional value but rather an aesthetic one, result of the intertwined relationship, created during the project, between morphological rules and tectonic processes.

ABSTRACT

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INDEX

PART I

PART II

PART III

ARCHITECTURAL BACKGROUD

ALGORITHM

DESIGN PROJECT

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High Rise City

From Chicago to the Global Skyscraper American Made From International Style to Post-Modernism The Global Skyscraper Urban sprawl and high-rise buildings.

Skyscraper Synthesis

Inroduction Evolutionary Multi-Objective Algorithm

.

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Theoretical Background Algorithm Limitation of Generic Solvers

66 Multi - Objective Optimization

New Structuralism Extreme Integration

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Genetic Algorithm

Repetition

70

Sydney Analysis

76

Barangaroo

88

GA Implementation

Building Regulation and SEPP 65

Genomes Phenotype Fitness Functions Generations Results

114 Structura Aggregation

Identical Copies Serial Production Continuous Differentiation Conclusion

Structure Analysis-Organizzation

122 Skin 130 Design Project

Parametricism 2.0

155 Physical Model 160 Final Remarks 164 Bibliography

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INDEX

INDEX

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PART I ARCHITECTURAL BACKGROUND

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1.1 High-Rise City

1.1.1 From Chicago to the Global Skyscraper.

High-rise buildings are the symbol of the modern and contemporary city. Conceived as a tool to increment the benefit from the land property and to solve urban sprawl issues, now they are also a symbol of the power of the city and their capacity to attract more investors and people. From its origins until now, skyscrapers always had a mutual and systematic relationship with the economic and cultural life of a city. Its development was a necessity for a fast-changing world based on the paradigm of mass-production. Moreover, it was an answer to the increasing number of people and economic activities populating the new urban areas. The ability to build in height it is now seen not only as the result

CBD Towers - Brisbane - 2015

01 - Architectural Background

of a city’s growing economy but it also acquired a symbolic value of prestige and power. Cities are now competing with each other in order to attract even more companies and investments. This phenomenon is particularly evident in the Middle East and in South East Asia. In fact, the geography of economic power does not draw anymore a line between a centre and a periphery. Over the years, the economic power ceased to be exclusively centred in the West and became more widened and dispersed. This had a tremendous effect on many cities, giving the birth to big urban areas and new skyscraper in cities such as Beijing, Honk Kong, Singapore etc.

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1.1.2 American Made

The interaction between economic growth, technological development, society and architecture found its physical expression in the construction of the first tall buildings happened at the end of the nineteenth century in the United States. Even though the high-rise

typology was born in Chicago, some of its forerunners elements are more evident and clear in the urban area of New York, especially in Manhattan. The increasing number of people moving to New York drove planners to adopt a grid system for the city. The grid was able to accommodate future developments and it was

New York grid aerial view - 19th century.

Reliance Building in construction, Chicago, 1890. John Root

Reliance Building completed, Chicago, 1895. Charles B. Atwood

used to better divide the plots for investors. After the adoption the new urban plan made of boulevards and avenues, Le Corbusier described New York as “the first place on earth at the scale of new times”1. The prizes of the new square plots rose to a mere extent; many and many people wanted to live in the hearth of the economic and financial centre of the new world, leading to the necessity to build an even denser city. A huge fire burned the majority of the buildings in Chicago in 1871. Thanks to this unfortunate event, a similar plot that was used for

Manhattan was adopted for this city with the goal of increasing the density of the metropolitan area. In order to compacting the urban environment in Chicago, engineers and architects took advantage of the new technological discoveries such as the hydraulic elevator, steel frame, air conditioning, mechanical plumbing and communication systems. The steel frame structure, first developed by Le Baron Jenney, was a valid solution to the prevention of a new disastrous fire. Furthermore, from a structural point of view, it also allowed increasing the height of the building without

1 Le Corbusier, 1938, “La catastrophe féerique”, in “L’Architecture d’Aujourd’hui”, Paris, Armand Magueritte, 1, pp. 12.

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thickening the masonry wall at the ground floors and opening large stained glass on the building façade bringing more natural light for the internal activities.2 The skyscraper is another typical application of the abstract procedure of American architectural culture, such as the grid plan for the cities. It is an indefinite device, destitute of proportions and unity; as Wright says, it is “a mechanical stratagem”, able “to multiply fortunate areas as many times as you can sell and resell the original land”3. Putting aside the comparison with the traditional visual habits, a new

way of conceiving the architecture was growing. The architecture of high-rise buildings is not anymore a set of harmonic lines, masses and voids but more an arithmetical operation, a multiplication, such as the grid urban plan for New York or Chicago. Both the grid plan and the serial repetition of the floors in a skyscraper are the symptoms of an adjustment of the principle that drove architecture to the one regulating the industry and the economy. After the World War I, the American economy grew fast as well as its population that moved

New York Skyline 2015. At the centre the Empire State Building, symbol of the American economic power.

2 Leonardo Benevolo, 2011, “Storia dell’architettura moderna”, Editori Laterza, Bari, pp. 225 – 240. 3 F.Ll.Wright, 1930, “Modern Architecture conference”, Princeton, Princeton University.

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City junction at night. The introduction of cars extend the city boundaries.

towards the main urban areas radically changing their faces. Giving the lack of a proper architectural culture in the US and the pursuit of freedom in many fields of society, American architects did not accept the interference of the international style. This refusal of the international style led American architects to rethink the classical heritage giving birth to what is called the “American eclectic style”. With a growing densification of the city-centre and the spread of the use of cars, international style seemed to give the best answers to address the issues posed by new

modern cities. Furthermore, the spread of the international style among American architects was also affected by the arrival of many European architects in the new continent. After the economic crisis of 1929, the international style was commonly spread in the majority of the American cities. The new skyscrapers were definitely different from the first ones. Standardization of the floor plan, new more efficient structural frame and the spread of the international style generated a new type of high-rise building. 4

4 Leonardo Benevolo, 2011, “Storia dell’architettura moderna”, Editori Laterza, Bari, pp. 225 – 240.

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1.1.3 From International style to Post - Modernism

After the World War II, western countries faced an incredible growth both economically and demographically. Cities were growing fast, they needed to accommodate a large amount of people and, at the same time, they had the resources to invest in complex and articulated buildings

such as skyscrapers. During the War, North America attracted several members of the international style movement from Europe who found themselves working in an architectural environment lacking of strong stylistic precedents. For this reason, architects such as Gropius,

Chicago Tribune Competition entries, Chicago, 1922. The entries were characterized by both eclectic and international style.

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Rockefeller Center, Reinhard & Hofmeister, New York, 1932.

Daily News Building, Hood & Mead Howells, New York, 1930.

Breuer, and Mies van der Rohe had the opportunity to conduct their architectural ideas, paving the way for a further spread of the international style in the US. New buildings with square rectangular footprint, grid façades and simplistic volumes emerged. Ornaments were rejected in contrast with the previous eclectic style in the American culture, and modern materials such as concrete, glass and steel gained a central role in the construction process. The international movement thought that the progress toward a perfected world was inevitable,

making the past obsolete, therefore their goal was a rational and scientific approach to art and architecture The new generation of architects aimed at “machine aesthetic� considering the high-rise building as a combination of technological and mechanical parts working together as cogs. Architecture, just like machines, had to rely on industrial mass-production. Moreover, their scientific approach, in a deterministic sense, sometimes refused to take into account the local particularities of the cultural, social and urban environment,

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elevating the building as a global and international solution to every architectural issues. In the “machine aesthetic” each element of the building was providing a solution to each architectural issue. The parts of the building machine were repeated several times and they were thought as independent parts working separately for the same machine. This idea is clear in Mies van der Rohe glass skyscraper. Even though the floor plate could be seen as a turning point from the standardized plans of the international style to

a more organic one, nevertheless, the essence of the building is not different from the past. The curved glass is just a juxtaposition of serial produced element on a regular steel frame structure. The structure, the very symbol of the technological progress, was highlighted by the transparency of the façade. During the sixties new technological discoveries in structural engineering changed the way skyscrapers were built. Fazlur Khan introduced the steel tube structural system allowing more freedom in the design of the plans. Its introduction opened new

The John Hancock Center, Skidmore, Owings and Merrill, Chicago, 1969. Example of the new technique of steel structural tube frame by Fazlur Khan.

Toronto Dominion Center, Mies van der Rohe, Toronto, 1969. Example of the modernist approach to High-Rise buildings

formal possibilities for the design of high-rise buildings allowing them to reach even higher heights and abandoning the rectangular and box shaped form. This innovation had important consequences over the Modernist paradigm that became overwhelmed by a new architectural sensibility that was looking for a more diverse formal representation made possible by structural innovations.5 Even this turn into a more free design did not change the underling standardization of the building. A refashioned modernism and the postmodernist cultures are still based

on the international style paradigm of a deterministic approach to the design elaborated during the first years of the twentieth century. Postmodernists are evolving the formal outcomes without changing the way the high-rise building is thought from the early stage of the design process.

5 Leonardo Benevolo, 2011, “Storia dell’architettura moderna”, Editori Laterza, Bari, pp. 639 – 647.

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1.1.4 The Global Skyscraper

In the closing decade of the 20th century, new economic and political scenarios shifted the global centre of cultural changes from the countries bordering the Atlantic, to those bordering the Pacific Ocean. The rapid increase of the population in the South East area created a new group of fast changing metropolis.

Cities such as Honk Kong, Singapore, Jakarta or Beijing are now facing new form of buildings and a need to assert themselves as unique in a global world. The last decade of the 20th century also marked the end of the Modernist paradigm of providing, thanks to a mass production system,

Tall building in the world 1982

Tall building in the world 2006

49,4%

32,2%

21,3% 20,2% 9,6%

23,9%

23,7%

20,2%

N. America

Asia

Asia

Europe

Europe

Others

N. America

Others

Number of High-Rise Building in 1982 and 2006 sorted by continents. Source: www.emporis.com

Burji Khalifa Dubai 2010

828 m

Taipei 101 Taipei 2004

Petronas Tower

Bank of Manhattam

Kuala Lumpur 1998

New York 1930

Woolworth Building New York 1913

442 m

Metropolitan Life Towe

New York 1909

319 m 187 m 55 m Singer Building

One World Trade Center

Manhattam Life Building

Empire State Buildin

Insurance Building

Chrysler Building

New York 1906

New York 1894

Chicago 1885

United States Of America

New York 1972

New York 1931

New York 1930

Asia

a cheap house for everyone. The need for new places in the hearth of the new metropolis caused the growth of land prizes. The global economic revolution of the eighties played an important role in this change. The financial movement of capitals gave to cities and buildings a new and crucial role in the economic development of countries. Consequentially housing and built environment acquired a new different role. From a mean to provide shelter, housing became a mean to generate financial returns. A building is no longer something to use, but to own – with the hope

of increased asset-value, rather than use-value, over time. Buildings become part of an economic exchange cycle: conceived for the lowest possible cost, traded for the highest possible sum. As Reiner de Graaf stated: “Modernism had a rational program: to share the blessing of science and technology, universally. Recent decades, however, have shown that Modern architecture can just as easily be deployed to work against its original ideology. No longer is the “economy of means” a way to provide buildings efficiently for the largest number of people, but rather a way to reduce

UAE

Tollest buildings during the years. Source: http://www.tallestbuildingintheworld.com/

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cost and maximise profits”6 .This new idea of the building as something that can produce profits led to an extreme densification of the urban environment regardless the quality of the living standards. vModernism and its successive evolutions revealed their inability to address the challenges posed by contemporary society. The complexity of the today urban environment can only be decoded with a non-deterministic approach. Skyscrapers emerge as parts of the new global city complexity and they are not only a static, monolithic mass that has to be mass-

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produced. Therefore, non-linearity of the skyscraper complex system requires a differential approach to its design able to address not only technical and aesthetic challenges but also environmental and social functionality ones.

1.1.5 Urban Sprawl and High-Rise Buildings

“In the next 20 years, we will face a massive movement of people to the cities. Depending on what we do to accommodate them, it could be a great opportunity or it could lead to an increase in social and environmental inequality”.7 This is how the Pritzker prizewinning architect Alejandro Aravena

describes the challenges that global cities will face in the next decades. It is commonly understood that modernist approach to urbanisms has become unfit to address the contemporary changes happened in today’s cities. The immense flow of people wanting to live and work in the biggest cities has become too

Honk Kong Residential Block, 2017. The increasing demand for a place in the new economic centers led to an extreme and uncontrolled development of the urban area.

Oshodi Market, 2015, Lagos. Third world cities are developing fast.

6 Reiner de Graaf, 2015, “The same architecture that once embodied social mobility now helps to prevent it”, Dezeen, https://www.dezeen.com/2015/05/07/reinier-de-graaf-opinion-idealsmodernism-architecture-social-mobility-capital-property-market-trellick-tower-park-hill/

7 Alejandro Aravena, 2016, Two billion more people will live in cities by 2035. This could be good – or very bad, “theguardian.com” https://www.theguardian.com/cities/2016/oct/19/twobillion-more-people-live-cities-alejandro-aravena-habitat-3

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big, widespread and uncontrollable to be organised using the old tools of modernism. Moreover, massive zoning brought to the rise of uncontrolled suburbs outside the reach of public transport, hospitals and all the other services that make the city a magnet. Finally, the more the mayors try to control the free and the dynamic market, the more they fulfil the chaos and the inequality of the urban environment. Contemporary market has the resilient ability to adjust itself to the need of the society whereas rational urbanism was trying to control it in all of its aspects. Therefore, this

method proved to be ineffective and counterproductive. 8 Cities themselves are products to be sold on the global market. International companies started rating cities based on several indices such as economic growth, stability, security, healthcare, culture, environment, education, public transport and so on. Local planners began to bend to the rankings in order to attract even more investors. In doing so cities are competing with each other to be the most feasible place for investors, companies, and people. Due to an out-dated agency between

Favelas, 2016, Sao Paolo. The uncontrolled development and the necessity of living next to the city is leading to the creation of informal settlements.

investor, designer and government, the original architectural vision is slowly eroded way to make way for greater economic returns. These decisions are validated through a series of metrics, creating a “tick box” culture to satisfy the lowest common dominator. If a building has a high net to gross efficiency and achieves a 6-star “Green Star” rating, the project is considered a success. Unfortunately, too often these measures underscore how contemporary architects validate their work. Stripped of its ideological dimension, buildings are presented as performative without

a comprehensive understanding of the overall fitness.9 Yet, the new mass of people moving towards the metropolitan areas could also be a resource both because of the wealth that it is able to produce and because of the benefits that it can have on contemporary environmental issues. Cities are better place for people to satisfy their need. Increasing the density of the urban area some services like public transport, healthcare, education are more effective. They can also provide more opportunities for jobs and investments for higher classes.

9 P. Wintour, 2015, Fitness of Politics Studio, University of Technology Sydney, Sydney, p. 2.

8 Ibidem.

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Subway Entrance Syndey. Fast and efficient public transport systems are the key to reduce the distances between the suburbs in a metropolitan area.

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Moreover, the dynamic character of the cities could offer the promise for social mobility.10 Nevertheless, the increase of density can also be positive for environment. Denser cities reduce the land use which means less hydrogeological risk. At the same time, more population leads to more efficient public transportations and public services in general resulting in the reduction of car usage and less co2 emissions. In conclusion, a solution to reduce social inequality and environmental issues is to increase the density. High-rise building could solve the

issues of a fast – growing city. On one hand, they are typically wasteful in energy when they are built and maintained, but on the other hand, they can be potentially more energysustainable than other buildings.11 In fact, their higher density and smaller footprints cause reduction in commute, urban sprawl and traffic. Moreover, their height and vast skin surface may enable them to take advantage of environment and natural light too.

Circular Quay by night, Sydney, 2016.

10 Alejandro Aravena, 2016, Two billion more people will live in cities by 2035. This could be good – or very bad, “theguardian.com” https://www.theguardian.com/cities/2016/oct/19/twobillion-more-people-live-cities-alejandro-aravena-habitat-3 11 Aryan Shahabian. (2015 Sept), “Integration of solar-climatic vision and structural design in architecture of tall buildings”. University of Applied Arts, Vienna.

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1.2 Skyscraper Synthesis

1.2.1 New Structuralism

The structural system plays a relevant role in the design of highrise buildings. It is also defines the final formal outcome. Over the last decades, structural design for skyscrapers was based on Fazlur Khan invention of the steel tube frame system. He divided it in two categories: interior and exterior structure. The first one have the major part of the lateral load resisting system located within the interior of the building while the latter located on the perimeter of the building. Khan conceived the structural building system, hence its formal representation, as a sequence of stacked, single story system or as a super cantilever.12 Furthermore, structure was designed to serve only its function. Wind loads, dead and live loads were the only purposes in

the structural design. This concept is still based on the determinist idea of the building as a machine where each part is solving each issue. Since the introduction of more complex structural issues, architects and engineers worked closely in a bidirectional relation. Traditionally they followed the logic of the development of form, structure and material in this order. This emerged clearly during the design process of skyscrapers. The structural design was addressing the form needs and the materialization followed this process. The introduction of new technological tools is triggering a new circular and continuous feedback based relationship between structural designers and architects. This new approach to the building design is called “New

12 Kate Ascher, 2011, The Heights: anatomy of a skyscraper, Penguin Press, New York, pp. 20 – 34.

Fibrous Tower 2 - Kokkugia - 2008

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Structuralism”. The turning point of this new paradigm could be found in the work that Jorn Utzon in collaboration with Ove Arup did for the Sydney Opera House (1957 – 73). In the final solution, the problem of the geometry of the covering tiles influenced the design of the rib structure and the overall form of the roof. This effectively reversed the traditional process to become “material, structure, form”.13 The highly interactive collaboration between architects and engineers during the last decades changed the traditional way to understand structural design

as an a posteriori method. Design engineering is now embedded in the early stages of the architectural design process, making more complex and dynamic forms possible for the building itself. A further step in this evolution sprung thanks to the new computation power. In fact, structural elements can now follow formal and syntactic logics and become part of the generative architectural design process itself. Farshid Moussavi and Daniel Lopez-Perez state: “Tassellation moves architectural experiments away from mechanistic notion

Beast - Neri Oxman - 2010. Structure, form and materiality are designed together.

of forms to machinic notions of system that determine how diverse parts of an architectural problem interrelate to multiply each other and produce organizations of higher degree of complexity”14. The characteristic of this new approach to structural design is that every kind of tessellation, patterns or ordered configuration can be used in generative and differentiated systems. In the design of the high-rise buildings the complexity of the structural issues play a crucial role in the overall design. At the same time, a new structuralist approach

could be used in order to generate an elegant and an efficient structural design. Therefore, several architectural practise are more often using algorithms such as Finite Element Analysis (FEA) or Genetic Algorithms (GAs) which can process complex efficient calculus while providing interesting formal results in accordance with architect’s design.15 In conclusion, new approaches in structural engineering and architecture play relevant role in designing process of high-rise buildings. Moreover, other aspects of the skyscraper are affected by

Sydney Opera House - Jorn Utzon in collaboration with Ove Arup - 1973. The Opera House is seen as the first junction point between the geometry speculation and the engineering necessitis.

13 Neri Oxman, 2010, “The new structuralism. Design, engineering and architectural technologies” in “Architectural Design”, special issue New Structuralism, London, John & Sons, 80 (4), pp. 14-23.

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14 Farshid Moussavi and Daniel Lopez-Perez, 2009, Seminar “The function of Systems”, Harvard Graduete School of Design, Cambridge MA. 15 Keith Besserud & Neil Katz & Alessandro Beghini, 2013, “Structural Emergence” in “Architectural Design”, special issue Computational Works, London, John & Sons, 83 (2), pp. 49-55.

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1.2.2 Extreme Integration

the structural design. A dense and thick structural frame can obstruct the passage of natural light inside the building. On the other hand, building cores, hence circulation and services are dependent variable of the structural equation since they can be understand as structural elements. The rationalist paradigm of the machine aesthetic has been overcame by a new organism-like one. Each part and function of the skyscraper is not addressed by each architectural element but it is the entire building organism now responding to the several issues that is has to face.

In 1969, Reyner Banham in Architecture of the Well-tempered Environment16 argued that, during its history, architecture followed the idea of structure determining the form. This approach is clear in the Postmodernist and Structural Expressionist culture. The first one aimed to hide structure and services

Evolutionary Computation - Moh Architects - 2006. It is one of the first attempt to use Genetic Algorithm and Finite Element Analysis to affect building ciruculation and structural frame system.

leaving a clear and clean façade while the latter wanted to show structural and mechanical parts for its own sake. Rejecting the PostModern paradigm of the unity of the façade, designers can experiment new architectural solution in order to integrate completely all the parts of the building. The façade

Top Left: The main museum of Los Angeles Art - Tom Wiscombe - 2017 Bottom Left: Flower Street Bioreactor - Tom Wiscombe - 2009 Right: Domino Sugar Rafinery Tower D - Tom Wiscombe - 2015 The skin is not a cover rather an integrated part of the building able for example to produce energy (bottom left) or, more in general, able to create an added aesthetic value.

16 Reyner Banham, 1969, “Architecture of the Well-tempered Environment”, University of Chicago Press, Chicago, pp. 11-17

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could be seen not only as a cover but also as an active and functional element of the whole building. This new approach could fundamentally change the design of high-rise building, moving away from the Post- Modern idea of the skyscraper as a machine made of singular elements to a new highly integrated organism. Architects should rethink the hierarchical relationships between the building elements. In biology, it could be easy to understand the mechanisms and the purpose of a single feature but what it is impossible to deeply understand

is the complex combinations of morphological feature in an ecosystem or in an individual.17 At the same time, architects should create a complex and interconnected relation between all the architectural systems in a building. Structure can affect how the light is perceived inside the space, a façade can produce or carry energy through the building, building mass can be designed to reduce the gain heat and at the same time the structural stress on the elements. Those systems are affecting each other in several ways so, they should be designed as they

are part of a single and continuous organism. In nature, different systems can achieve an optimal solution for different issues of the same ecosystem. On one hand the pursuit of singular optimum solutions provide benefit for the whole system on the other hand this multi-optimal space represent the possibility for a system to be capable of evolve and adapt to several external conditions. If local condition changes one system could fail to provide its optimal solution but another one could prove to be even better of the previous. This evolution requires a feedback loop able to produce local inefficiencies and mutation that only later reveal their advantages. Skyscrapers are the architectural

representation of the contemporary complexity. In the past, they were conceived as machines and were produced with the same paradigm of the mass-standardization chain. The modernist approach resulted to be outdated and a new method is now emerging. Thanks to the new informational and mechanical technologies, structure, façade and services are integrated together blurring their differences and achieving a more fit building able to adapt to the singularities of the urban environment on which the building is designed. This new architectural paradigm can show its potentiality in a world that is no more based on the identical reproduction but on the differential variety.

“Excesses and messy overlaps of form and function allow multiple types of work to be done, from structural to environmental to ornamental”.18

Agamidae Lizard Australia. The skin’s aility to store and then provide water to the whole organism is hidden by the complexity and messiness of the pattern.

17 Tom Wiscombe, 2010, “Extreme Integration ” in “Architectural Design”, special issue Exuberance, London, John & Sons, 80 (2), pp. 78-87.

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18 Tom Wiscombe, 2010, “Extreme Integration ” in “Architectural Design”, special issue Exuberance, London, John & Sons, 80 (2), pp. 78-87.

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1.3 Repetition

1.3.1 Identical Copies

In the pre-mechanical age, architecture was in both designing and making process. Architects were something in between of designers and builders and their ideas were continuously changing during the making process. Moreover, because of the notational and technical issues emerging during the process, the early design of the architect was substantially different from the final artefact. Leon Battista Alberti was probably one of the first architect to try to solve the division between the project and the artefact aiming for an identical reproduction of its intellectual ideas. His pursuit for identical copies brought him to identify new notational forms.19 Alberti tried to reach the identical reproduction of almost everything. From text and images to drawing and design. What he wanted

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“The Story of Post - Modernism “ - Madelon Vriesendorp (OMA) - 2011

to really achieved was an exact and punctual communication of architectural styles, parts, building techniques etc. Nevertheless, he knew that the transmission of images could lead to some mistakes of communication, especially for builders. He also understood that alphabetical texts and numbers were able to spread in space and time without have been mistaken. Following this idea, he wrote his manuscript treatises without images or illustrations. In this way, the readers could understand the proportion and the style of an architectural element e.g. a column, without looking at an image but just reading the rules of the relationship between the parts.20 Two important features emerged from this idea. On one hand, Alberti tried to separate the designing from

19 Mario Carpo, 2011, “The Alphabet and the Algorithm” The MIT press, Cambridge, pp. 5120 Ibidem pp. 50 -68.

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the making process, thus leading the way to the modern vision of the architect as the only author. On the other hand, the definition of the column by its notational relationships was producing not a single object but a variety of several items belonging to the same family of the column. Analysing his idea from a vantage position, as we are today, we could even say that Alberti’s definition of column is a “procedural algorithm” and the results it is a class of object hence an objectile.21 As Mario Carpo stated in his article on Architectural Design:

“Parametricism in architecture has a much longer history than that, and the true precedent to today’s computational variability must be found in the architectural theory of pre-mechanical civilization, which in the West include classical antiquity as well as the Middle Age.”22 The endless pursuit of exact replication that Alberti tried to achieve still represents one of the most important and significant milestone in the development of the modern history of art, science, culture and technology. Furthermore, his idea of building by notation weakened the differences between the project and the building, still determines the contemporary architectural practice.

Das Buchlein von der Fialen Gerenchtigkeit - Matthaus Roriczer - 1486. Roriczer was one of the first author to draw diagrams but at the same time he was explaning the geometrical rules of architectural elements as in the medieval and classical tradition.

1.3.2 Serial Production

The tangled relation between machines and architecture during the end of the nineteenth century made the Alberti’s dream of identical reproduction reality. The printing press showed the possibility to replicate the same text and, more importantly, the same image several time. Yet its peak was reached

during the industrial revolution when new technologies were assisting the serial and identical reproduction of the same object. One single object copied repeatedly and possibly infinitely. Thanks to the design of a single matrix, it was possible to generate an incredible amount of the same product, the

Ville Radieuse - Le Corbusier - 1930. Mass standardization proccess was not only an architecture feature but also an urban quality.

21 Mario Carpo, 2016, “Parametric Notations”, in “Architectural Design”, special issue Parametricism 2.0: Rethinking Architecture’s Agenda for the 21st Century, London, John & Sons, 86 (2), pp. 24-29. 22 Ibidem.

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more the quantity, the cheaper the object itself. In this way, industrial standardization was producing an economy of scale. The key factor in the new economic system was that, in order to obtain profits, machines should produce a large quantity of the same object. The sameness is the engine that underlie the success of the mass standardization. Mass society was characterized by singularities and their repetition in order to maximize profits. The idea of sameness established itself in the cultural discourse of the machine age including architecture, art and urbanism. Adopting the new ideas of the industrial revolution, architecture started to apply the same rule of

the mass standardization process to its design process. From the first grid plan in Chicago to the birth of the skyscrapers, architects realized the necessity to conform to the new industrial society. Skyscrapers were the quintessence of this new paradigm. They were designed in order to increase the land profits by stocking several identical floors sequentially. Moreover, skyscraper were possible only thanks to the new development in the structural and service industry.23 Buildings and cities became the product of a mass production machine providing the same object repeatedly. The first geometrical models during the industrial revolution were the same as the classical ones.

After World War I, Le Corbusier and the others Modernist designers thought that architects should invent new architectural forms, made to deal with the new tools of mechanical mass production as well as new urban forms able to accommodate the increasing demand for denser city and to deal with the new mass transportation systems. Consequently, the conception of the architecture as a mass production tool itself flattened the heterogeneity of the local styles around the world.24 Despite the technological advances, the notational regime was imposing upon architects the use of few geometrical elementary elements, such as straight line,

right angles, squares and circle all based on the Euclidean geometry. Symptomatic of this geometrical limitation is what Le Corbusier in a summary on basic geometries (lines, regular surfaces and elementary solid) affirmed proudly: “This is geometry”25. The geometrical architecture repertoire of the time was based on the possibility or not to physically reproduce the geometry itself. In conclusion, during the mechanical era, notational issues and the continuous relation between architecture and the mass production chain made architectural outcomes similar to a standardized product. Therefore, their identification was based on visual identicality.26

Sydney skyscraper.

23 Leonardo Benevolo, 2011, “Storia dell’architettura moderna”, Editori Laterza, Bari, pp. 225 – 240.

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24 Branko Kolarevic, 2003, “Architecture in the Digital Age: Design and Manufacturing”, Spoon Press, London and New York, pp. 13-14. 25 Le Corbusier, 1925, “Urbanisme”, Ed Crès, Paris. 26 Mario Carpo, 2011, “The Alphabet and the Algorithm” The MIT press, Cambridge, pp. 3-35.

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1.3.3 Continuous Differentiation

“The modern power of identical came to an end with the rise of digital technologies. All that is digital is variable, and digital variability goes counter to all the postulates of identicality that have informed the history of Western cultural technologies for the last five centuries” 27

The changes brought by the digital revolution affected all the aspect of the contemporary society, from science to architecture. It radically changed our culture

even though during the first years it was seen just as a tool to fulfil the paradigm of the mass standardization process. The concepts of singularity and stability

that characterized the mass society have been replaced by multiplicity and variability. The informational era allowed the development of a culture based on non-linearity and indeterminacy in many aspect of the society.28 During the early nineties, there was the belief that digital revolution would have changed the way we were able to make things in art, science, industry and all the other aspects of society. The progress in technological field was thought as a tool for incrementing the production in every field. Even though many research clusters in science, digital

An Evolutionary Architecture - John Frazer - 1995

27 Mario Carpo, 2011, “The Alphabet and the Algorithm” The MIT press, Cambridge, p X.

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Embryological House - Greg Lynn - 2006. Source: www.parametricmonkey.com

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art, architecture, economy were aware of the potentiality of the new technological discoveries, for the mass industry digital revolution was only a way of making things faster and cheaper. Yet, the new digital technology influenced art and architecture as well. Designers started using digital tools to solve all the problems related to geometrical and notational issues. In 1925, Le Corbusier designed the church at Ronchamp. According to the architect design, the building was supposed to be sculptural, made of irregular volumes but the difficulties of the representation led

28 Patrik Shumacher, 2008, “Parametricism as Style – Parametricist Manifesto”, www. patrikshumacher.com

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engineers to make some changings especially in the roof design. The French architect faced quite the same issues that Alberti did. The impossibility to exactly reproduce his design idea. Thanks to the new developments in the field of digital technologies, complex, curve and folded geometries not only were able to be drawn but also built. Thanks to the new digital tools a full universe of new form possibilities opened to the designer’s eyes: “all that is digitally designed is, by definition and from the start, measured, hence geometrically defined and buildable.”29 A crucial

example is the Guggenheim museum of Bilbao by Frank O’ Gehry. The geometrical irregularity could be seen as first step into Parametricism but this idea would be misleading: in fact, digital tools were used just as a mere means for represent the physical model. As Mario Carpo stated “Gehry’s pantographic process did not mark the end, but the climax of the notational paradigm carried over, through digital tools, from an older world of simpler geometries into a new universe of “free form” and unprecedented formal complexity”. In these sense computers were first

seen as tool to make identical copies fulfilling the rational dream of the modernist movement and the mass standardization production. 30 During the following years, new theories in architecture started to emerge; computers could also be used to design and build variable objects and not only tridimensional copies. The starting point of this shift could be found in Gilles Deleuze’s book “The Fold: Leibniz and the Baroque” when the philosopher brought the attention to the continuity of the differential calculus of the Leibniz mathematic. The potential of computers to elaborate

Guggenheim Museum Bilbao - Frank Gehry - 1998

29

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Romanesco Broccoli, Lorenz attractor, Flocking birds. Source: www.parametricmonkey.com

Mario Carpo, 2011, “The Alphabet and the Algorithm” The MIT press, Cambridge, pp. 34.

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and manipulate complex differential equations caught the attention of the designers. Deleuze together with Cache presented a new idea of the object in the digital era: “The objectile is not an object but an algorithm – a parametric function which may determine an infinite variety of objects, all different (one for each set of parameters) yet all similar (as the underlying function is the same for all)”. 31 Gradually, the attention shifted towards the consequences of a fully integrated design and production chain. The new notion of “objectile” opened the possibility to create

30 Mario Carpo, 2011, “The Alphabet and the Algorithm” The MIT press, Cambridge, pp. 33-36. 31 Mario Carpo, 2011, “The Alphabet and the Algorithm” The MIT press, Cambridge, pp. 40.

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a mass production series of heterogeneous products. Parametric design with digital fabrication processes are the foundation of the nonstandard seriality and they caused the triviality of the economies of scale. Mass-standard production is based on the ability of the system to replicate a large amount of the same object increasing the profit proportionally with the number of replications. The arrival of the digital fabrication made mass-standard productions matrixes, casts and molds no more necessary for the mass production. The new nonstandard seriality is taking place and it is based on the possibility to create an infinite diverse but similar products. Contemporary society is replacing the ideas of repetition

and singularity with the continuous differentiation and heterogeneity.32

Pre-mechanical age was distinguished by the technological impossibility of visual serial reproduction while today’s variability in the production process is generating visual differences with few similarity traits. In this context, the ontological identification of the object was based only on a visual resemblance. With the revolutionary power of the industrial machines (even with the invention of the printing press), production was able to replicate standardized object several time leaving the identification on a visual identity base. This was the direct consequence of the pursuit of a mass standardization production process based on the economy of scale. Digital tools, at the beginning of their existence, have been used to amplify and aid the standard production chain. After a while, they were able to generate objects whose identification was based on algorithms. What is digital is variable and this goes in contrast to what the machine age has produced during the last two centuries. Alberti’s description of the column has little

32 Branko Kolarevic, 2003, “Architecture in the Digital Age: Design and Manufacturing”, Spoon Press, London and New York, pp. 13-14.

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features in common with today’s algorithms; those features were lost during the machine age. The predictable relationships between design and representations are abandoned in favour of computationally – generated complexities. Models of design capable of consistent, continual and dynamic transformation are replacing the static norms of conventional processes. The new post- Fordism society, the mass customization process, the call for a new architectural style able to provide answer to the social and environmental issues, represents the background in which Parametricism is working. According to Patrik Shumacher “it is the solid and the only answer to the complexity of the new social and economic dynamics of the informational age” . At the same time, Parametricism lacked in showing its superiority compared to today’s architecture practice regarding the contemporary issues such as social, engineering and environmental performances.34

34 Patrik Shumacher, 2008, “Parametricism as Style – Parametricist Manifesto”, www. patrikshumacher.com

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1.4 Parametricims 2.0

“Parametricism is architecture’s answer to contemporary, computationally empowered civilization, and is the only architectural style that can take full advantage of the computational revolution that now drives all domain of society” 35

Contemporary world, in all its aspect and fields, can only be understood by shifting our point of view from a deterministic way, traditionally used by Modernist movement, to a nonlinear one. Computation is a necessary accomplice in working with complex systems, but, as Schumacher stated in his article “Parametricism 2.0”, it failed to show the practical advantages in its method and especially in the domain of social functionality. At the same time, Parametricism is the only architectural methodology and style able to decode the contemporary world, to understand the computational revolution we are facing, and to generate complex and aesthetically interesting results. 36

The society that was based on mass production, singularities, stability is now based on heterogeneity, continuous differentiation and dynamicity. The post Fordist society is now characterized by a high degree of complexity, global scaled interactions and an enormous level of indeterminacy in several social phenomena. This is the world that contemporary architects are facing and confronting. Therefore, the task is to generate equally complex systems that are continuously differentiated producing an architecture able to adapt to local variations of the society and the environment. New computational tools made new formal representation possible. Moreover, they also provided a

35 Patrik Schumacher, 2016, “Parametricism 2.0: Gearing up to impact the built environment” in “Architectural Design”, special issue Parametricism 2.0: Rethinking Architecture’s Agenda for the 21st Century, London, John & Sons, 86 (2), pp. 8-17. 36 Ibidem.

Chhatrapati Shivaji International Airport Terminal 2 SOM Architects - 2014

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completely new way to think and understand contemporary world. Arguing that it is not only the new tools that are changing the architecture, Patrik Shumacher says: “This is evidenced by the fact that late modernist architects are employing parametric tools in ways which result in the maintenance of a modernist aesthetics, i.e. using parametric modelling to inconspicuously absorb complexity. Our parametricist sensibility pushes in the opposite direction and aims for a maximal emphasis on conspicuous differentiation”.37 Natural systems are the results of an organized and low-governed complexity. Tom Wiscombe compares his idea of extreme integration to the jungle complexity, where it is impossible to understand its mechanism in a deterministic way but it shows a strong ability to adapt to changes of local conditions. Moreover, every part of the ecosystem even though it might seem irrelevant from an optimization point of view, it can reveal its importance when the local conditions vary.38 At the same time, the new paradigm in architecture should be able to reach an interarticulation of the sub-systems. In contrast with the space paradigm of Modern architecture, Parametricism is looking at fields. Fields are based on local qualities

that can inform the architecture process and can lead to the emergence of unexpected set of results. This new logic can be used to address the complexity and the dynamism of the contemporary society. The ability and the potentiality of this new method in architecture are seen, wrongly, as a simple expression of artistic or technophilic exuberance. Parametricism lacked in emphasizing the discussion and explication of its practical advantages in adaptive structural and tectonic differentiation, in environmental issues as well as in the domain of social functionality. Parametricism should now focus on these aspects in order to mature and be accepted as a serious contender for global best practice. 39 Skyscrapers were the quintessential of Modernist architecture and of the standardization paradigm. Last decade showed the inadequacy of the recent architectural approaches leading the way to a more computational empowered one. Parametricism is the only style able to decode the skyscraper complexity and to address its social, environmental and structural issues at the same time basing its methodology on “parameters that matters” 40

37 Patrik Shumacher, 2008, “Parametricism as Style – Parametricist Manifesto”, www. patrikshumacher.com 38 Tom Wiscombe, 2010, “Extreme Integration ” in “Architectural Design”, special issue Exuberance, London, John & Sons, 80 (2), pp. 78-87. 39 Patrik Schumacher, 2016, “Parametricism 2.0: Gearing up to impact the built environment” in “Architectural Design”, special issue Parametricism 2.0: Rethinking Architecture’s Agenda for the 21st Century, London, John & Sons, 86 (2), pp. 8-17. 40 Patrik Shumacher and Michael Hansmeyer - F’14 SoA Lecture Series, “The New How” (112414). (2014). Retrieved May 12, 2015, from https://vimeo.com/112854141

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PART II ALGORITHM

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2.1 Genetic Algorithms

2.1.1 Theoretical Background “Natural selection is a mechanism for generating an exceedingly high degree of improbability.” Sir Roland Fisher

In order to explain what a Genetic Algorithm (GA) is and how it works, it is necessary to refer first to the concept of adaptation. In fact, this concept could be described as a feedback process in which external changes in an environment are mirrored by compensatory internal changes in an adaptive system. An interesting example of the adaptation process could be seen in the world of complex systems. Indeed, adaptation emerges where the interactions between subunits change and at the same time, their changes affect the environment that in turn feeds back the new information to the system. Another step to fully understand a GA is exploring how the natural evolution works. Charles Darwin’s evolution theory was able to explain

an incredible amount of natural phenomena with very few logic and biological rules. The predictive and explanatory power of evolution and natural selection has shed light on every facet of biology, from the microscopic scale of how bacteria quickly adapt and become resistant to new drugs all the way up to the macroscopic scale of the distribution and interrelatedness of whole species. Adaptation in biological term is a part of the equation: Adaptation = variation + heredity + selection.1 This new definition of adaptation differs from the original Darwinian one because it introduces the idea of heredity. The ideas of parallelism and adaptation play a fundamental role in the definition of the previous

1 Gary William Flake, 1998, “The computationao Beauty of Nature”, MIT press, Cambrindge, Massachusetts. pp. 339 - 342

Emergence Seminar, Architectural Association, 2014

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equation. According to the New Darwinian theories, variation is not referred to the single individual rather to the multiplicity individual. In this context variation is a process that refers to how individuals in a population can differ from each other. Thus, parallelism and spatial multiplicity are essential ingredients in the algorithm of evolution. On the other hand, the iterative process of hand down traits from the parents to the children is what heredity is about.2 According to the theory of evolution if we would live in a world with infinite resources evolution

would have never took place since adaptation was not required. Thanks to finite characteristic of the world species and individuals, place their hopes in their ability to reproduce so to pass through the generations their traits. The “survival of the fittest” is a tricky sentence with an ontological mistake: the fittest are the survivals. A better sentence could be “the survival of the reproducers”. In fact, as Richard Dawkins said: “you and I can proudly make the claim that every one of our ancestors – without exception – survived long enough to reproduce.” The ability to reproduce is often seen as a group of positive

and virtuous characteristics but in reality, what really counts in natural selection is an organism’s ability to reproduce. The last concept that has to be introduced for a complete description of Genetic Algorithm is coevolution. This express the ability of two species to mutually adapt in a circular manner with one species’ influence on another ultimately returning to the first species in a

feedback loop. An example is the lion and the gazelles coevolution. During the last centuries lions became better predators forcing the gazelles to improve their speed and elusiveness. 3 Concluding, the same concepts of evolution, natural selection, heredity, variation and adaptation are the fundamentals of the development of Genetic Algorithm.

The relationship between bats and moths is a clear example of coevolution.

2 Gary William Flake, 1998, “The computationao Beauty of Nature”, MIT press, Cambrindge, Massachusetts. pp. 339 - 342

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3 Gary William Flake, 1998, “The computationao Beauty of Nature”, MIT press, Cambrindge, Massachusetts. pp. 339 - 342

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2.1.2 The Algorithm

The invention of Genetic Algorithm could be found in the works of John Holland in the 1960s but it started to be used and researched during the ‘80s. M o r e than any other evolutionary solver, Genetic Algorithm is closely similar to biological evolution since it maps data into strings in the same way as the DNA. Those strings, as it happens in natural evolution, are subjected to operations such as mutation, crossover and mating. The main purpose of Genetic Algorithm is to solve problems which their solution is not uniquely determinate or is too time consuming to find hence impossible. Therefore, generic solvers such as GA are used to solve mathematical problems belonging to the classes of P, NP, NP-hard and NP-complete.4 Before understanding what is the mechanism behind a GA it is important to define properly what a problem and a solution are. According to David Rutten: A fairly hard-core definition of both would

be that a problem is the extrusion of a system phase space, and a solution equals high ground in this newly created landscape. A phase space is the collection of all the possible states of a specific system. For example if we consider a hanging chain fixed to the extremes then the phase space is the group of all the possible deflection and position of the chain itself. The phase space is a variable based definition. In fact, a system with one degree of freedom will be represented with a phase space in one dimension; another one with three degree of freedom is represented with an extruded plane and so on.5 Another important part of the evolutionary solver is the “fitness function”. This function computes the desirability of any point in a phase space. Therefore, is the designer to choose the function hence the trait that he likes better. Solutions with an higher value are preferred to the once with lower values. The phase space is the collection

4 David Rutten, 2014, “Navigating multi-dimensionallandscapes in foggy weather as an analogy for generic problem solving”, 16th International Conference on geometry and graphics, Austria, 5 David Rutten, 2013, “Galapagos: on the logic and limitations of generic solver” in “Architectural Design”, special issue Computation works, London, John & Sons, 82 (2), pp. 132 -135.

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of all the states of the system while the fitness function is the method for rank the quality, or better, the desirability of, theoretically, every states. Joining the two ideas, it is possible to define the concept of fitness landscape. It is the representation of the ranking value for every state of the system. For a two dimensional problem it could be represented as shown in the figure above. The peaks are the better solutions at the problem. As I will explain better in the following paragraph, some topological features of the fitness landscape represent obstacles for some kind of

generic solvers.6 In theory, it is possible to provide a fitness value for every state of the system. This approach could be possible for problems with a really small phase space in which there is enough time to evaluate all the solutions. Unfortunately this approach is not possible for problems with a vast phase space since the time required in order to analyse all the solution is incredible bigger of the time of the universe. For this reason Genetic Algorithm, as other generic solver such as Simulated Annealing, are used to find a local optimal solution to the

6 David Rutten, 2013, “Galapagos: on the logic and limitations of generic solver” in “Architectural Design”, special issue Computation works, London, John & Sons, 82 (2), pp. 132 -135.

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problem in a relative small amount of time. Genetic Algorithm is a type of solver which uses biological and evolutionary operators in order to find the higher peaks on the fitness landscape. Generating a random initial population, GA, will evaluate the fitness values of the individuals and after processes of natural selection, mating, crossover and mutation they will create a new generation of individuals (states of the problem) that with an high degree of probability will have higher value of fitness. This process is repeated for a certain amount of

time until a fairly and local optimal solution is found.7 In order to explain the GA process it will introduced an example. Supposing to evolve a string into a specific English (Furious green idea sweat profusely) phrase the GA doesn’t know the phrase, it is only allowed to make guesses and to give a “fitness” for every string. The definition of the “fitness” is playing a key role into the GA process. As I stated before it is the desirability level of a certain state of the system. In this example, the first fitness value is the percentage of correct letters. It is possible to define three values

Initialize the population, P

create an empty population, P’ Select 2 individuals from P based on some fitness criterion

f

raw

f

scale

f

norm

=

N° of correct letter Lenght of target string

(1)

Optionally mate, and replace with the offspring Optionally mutate the individuals add the 2 individuals to P’

Repeat until P' is full let p now be equal to p’ Repeat for some lenght of time

=

i

=

2

f scale

(2)

f scale i ∑

n j=i

f scale j

(3)

t1: P' t2: P'' . . . tn: Pn

Left: Example of a simple GA. Right: the three basic equation of the example

7 Gary William Flake, 1998, “The computationao Beauty of Nature”, MIT press, Cambrindge, Massachusetts. pp. 339 - 342

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Parent 1 Parent 2

A

B

C

D

E

F

G

T

U

V

W

X

Y

Z

Crossover Point

"furious green ideas sweat profusely"

Alphabet letter: 26 Space Dimension: 1

27 characters Child 1 Child 2

A

B

C

D

X

Y

Z

T

U

V

W

E

F

G

Crossover Point

N° of possible strings with lenght of 35 letters: 27^(35)

Left: Example of a cross-over. Right: Phace space of the phrase example.

where the last two are function of the first one (opposite page.). The first function will evaluate every string giving as a fitness value the percentage of correct letters. Consequentially, we would like to unbind the fitness value from the length of the target string. In this case we can easily overcome the constrain of the length of the phrase. The function (2) has an interesting property: given two strings if one has more correct letters than the other one, the string closest to the target will receive a “bonus”; it will be two time more fit than the other will. Furthermore, the final step is to normalize the fitness (3). In this way, the selection could be made

regardless the magnitude of the fitness (1) and (2). 8 One of the most important part in GA is how selection is performed. One option, still related to this example, is to let the 50% of the population to reproduce. This approach has a critical problem. Some traits of the remaining 50% could be useful to the evolution process. In this way, we will throw away genetic diversity that could contribute to the evolution process. A better method is to normalize the fitness score. In this way, every individual has a chance to survive. The probability of surviving is higher if his fitness value is higher. It is possible now

7 Gary William Flake, 1998, “The computationao Beauty of Nature”, MIT press, Cambrindge, Massachusetts. pp. 339 - 342

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to randomly select individuals to reproduce for the next generation. The next operation in GA is to optionally mate selected individuals. Optionally could means that either we allow both parents to survive in the next generation unchanged or we mate the two offsprings that live in the next generation. Mating in the simplest case means crossover. When two different strings are crossed, the offsprings will be different from their parents but at the same time, they will contain part of the genetic materials of their parents. There is a chance that the child is less fit than parents are, but this setback could last for only one generation. That’s the reason why allowing the fittest 50% of a population is not always a good solution.8 The last operator that needs to be presented is mutation. Mutation represent the possibility to randomly introduce a variation in the string of some individuals in the population. If the mutation rate is small then most member will have zero mutation applied to them. Mutation allows introducing a completely new genetic material in the population. The new genetic material could lead to a fitter solution. In fact high mutation probability and rate are

good solutions to overcome small peaks in the fitness landscape. It could happen that during the process a generation tend to get stuck in some peaks without looking for a solution in different place where the fitness value is higher (See the next paragraph). Randomly changing some traits in the generation could bring the individuals to explore a new area of the landscape finding a higher peak. In conclusion, using a smaller population size would have resulted in the GA taking more generations to solve the problem. In any event, as the problem difficulty increases, so must the population size and number of generations increase. Moreover, strings with equal fitness may consist of completely different letters. This means that crossing such pair could result in a drastic improvement in the average fitness.9 One reason why the stringfinding problem is so easy to solve is that each individual character has a clear ideal value, that is, the correct value for the target. In the real world problems (biological or technological) fitness is a function of many things that are conditionally beneficial and harmful, depending on the context in which it exists.

8 Gary William Flake, 1998, “The computationao Beauty of Nature”, MIT press, Cambrindge, Massachusetts. pp. 339 - 342 9 David Rutten, 2014, “Navigating multi-dimensionallandscapes in foggy weather as an analogy for generic problem solving”, 16th International Conference on geometry and graphics, Austria,

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2.1.3 Limitation of generic solvers

Algorithms and solvers in general, cannot be considered as optimal solution finders. Problems can have different shapes, different sizes and also different nature. Some of them have a solution, some do not, while others have it only if a solution is proposed. In other words, in this case, the solution exists and

it is possible to recognize it, but it is impossible to find it in a reasonable amount of time, where reasonable amount means, in mathematical terms, “before the universe ends”. While analysing pro and cons of Genetic Algorithms it is important to bear in mind that it is absolutely not sure that a solver will find the

Geometry under - constrainedness. Large plateau. David Rutten 2014.

best solution in a finite amount of time. There could not be at all. The aim of problem solving in these cases is to find a solution (one of many) of sufficient quality in an acceptable amount of time. The number of iterations needed by a solver in order to find an acceptable solution depends considerably on the topology of the fitness landscape. For example, solvers can be confused when facing large horizontal plateaus because it is not obvious for them in what direction they should move towards. Solvers can also get trapped by a certain type of landscape shape.10 Gaps

can restrict solvers’ movements, fractal terrain can scatter the solver momentum. In this regard it is important to remember that the shape of the landscape, or rather its topology, is the direct result of the fitness function and thus its shape is at the mercy of the programmer that defined the said function. Next paragraphs will discuss two common generic algorithms simulated annealing and genetic algorithm - both based on reallife processes: one physical, one biological. We will highlight their strengths and weaknesses especially when it comes to face the backdrop

Up: Examplde of fractal fitness landscape. Bottom: Landscape with discontinuities. David Rutten 2014.

10 David Rutten, 2014, “Navigating multi-dimensionallandscapes in foggy weather as an analogy for generic problem solving”, 16th International Conference on geometry and graphics, Austria,

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of different landscapes topologies and geometries. Simulated annealing uses the theory of thermodynamics. In particular, it uses the process of crystalline matrix formation occurring when molten metal is allowed to cool. When the atoms cool down, they start banding together into tiny crystals that grow larger as the temperature drops. This process can be described by a set of equations, which can in turn be employed to find peaks in a landscape. The way annealing solver progresses is by jumping randomly across the landscape in ever- decreasing steps. If it does not accept the new location, perhaps because it is worse than before,

it will revert to the previous one. Eventually, all jumps will be very small and it will be very picky about accepting new states. The lifetime of the solver can thus be divided into two parts: first, it tries to find promising high ground, and then it will fine-tune its position in order to find the highest peak associated with this high ground.11 According to many programmers, simulated annealing has few drawbacks and the problems for this solver are commonly founded in other solvers too. It is also important to underline that, often, topological drawbacks such as plateau are caused by a poorly formulated fitness function.12 Simulated annealing can deal very well with

discontinuous and fractal terrain. One surprising characteristic of simulated annealing is that it will sometimes deliberately move to a worse solution. The benefit of being willing to choose, even temporarily, a worse solution is that sometimes it allows the sampler to jump out of a local optimum and find a higher position that is more than one move away from the starting point. Yet, the possible length of the jump is reduced during every iteration; the algorithm becomes less and less willing over time to accept a move to a lower quality tensor. We can, thus, conclude that annealing is better suited for rough landscapes and when long jumps are needed. Genetic algorithms are more complex than simulated annealing and have different pros and cons. The first con is time consumption. Evolutionary Algorithms are slow. A single process can run for days or even weeks. Given the specific nature of this algorithm, the solution process can became extremely complicated especially when there are set-ups that require a long time in order to solve a single iteration. Secondly, Evolutionary Algorithms do not provide a guaranteed solution. Unless a predefined “acceptable” value is specified, the selection process will tend to run on indefinitely, “never reaching The

Answer, or, having reached it, not recognizing it for what it is”13 The positive aspects of genetic algorithms can be grouped in three categories. First, they are very flexible and can be adapted to different problems and situations. Second, evolutionary algorithms are also forgiving. This means that, given the typical process of breeding selection, partner selection, coalescence algorithms and mutation into a genome, even a badly formulated function can lead to acceptable results. Third, the process itself allows a great degree of interaction with the user. “The run-time process is highly transparent and browsable, and there exists a lot of opportunity for a dialogue between algorithm and human. The solver can be coached across barriers with the aid of human intelligence, or it can be goaded into exploring sub-optimal branches and superficially deadends”14 Concerning the landscape geometries and their relations with genetic algorithms, experts generally agree that evolutionary solvers generally perform better in landscapes with many peaks with small basins of attraction. Unlike annealing, evolutionary solvers do not jump across the landscape in search of peaks.15

Geometry of over -constrainedness. David Rutten 2014. 11 David Rutten, 2013, “Galapagos: on the logic and limitations of generic solver” in “Architectural Design”, special issue Computation works, London, John & Sons, 82 (2), pp. 132 -135. 12 David Rutten, 2014, “Navigating multi-dimensionallandscapes in foggy weather as an analogy for generic problem solving”, 16th International Conference on geometry and graphics, Austria,

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13 David Rutten, 2011, IEatBugsForBreakfast blog, https://ieatbugsforbreakfast.wordpress. com/?s=fitness+landscape 14 Ibidem. 15 David Rutten, 2014, “Navigating multi-dimensionallandscapes in foggy weather as an analogy for generic problem solving”, 16th International Conference on geometry and graphics, Austria,

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2.2 Multi-Objective Optimization

2.2.1 Introduction

Genetic Algorithms are a powerful tool in searching local optimal of a given problem. Nevertheless, GA are based on one single fitness function meaning that only a single solution can be maximized or minimized. Realistically, mathematical problems usually depends on more than a single criterion. Evolutionary multi-objective (EMO) algorithms are now being used in order to maximize or minimize two or more functions at the time. Those algorithms uses the same principle of the GA but they search the phase space of the problems providing many extremes solutions that have a high value of fitness. The key factor in this approach is the choice of the method for search a

combination of optimal solutions (objectives) at the same time. The preference of a group of solutions for another is based on different process: - Decision making before search ( define single objective and decide by weighting the objectives) - Decision making after search ( search Pareto set first, then choose from solutions) - Decision making during search (guide search interactively by manual assessment) - Combination of the above In this work, a decision making after search approach will be used comparing all the Pareto nondominant solution of the space.16

16 Robert Vierlinger, 2013, “Multi-Objective Design Interface�, University of Applied Art Vienna, Vienna, pp. 31-33

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2.2.2 Evolutionary Multi-Objective Algorithm

Solutions dominated by A

Pareto dominant a solution B, if A is at least as good as B in all objective, and superior to B in at least one of them. A Pareto optimal solution means that given two different and contradictive objectives, one goal cannot be increased without decreasing the other one. Hence, this method could be seen as

Objective 2

Objective 2

As it was stated before, a MultiObjective Optimization (MOO) problem has several method for determining the desirability of a given state. In this work, the focus will be on the Pareto Dominant approach. Considering a set of two solutions to a two-objective problem a solution A is said to

a trade-off between the two objectives. The set of all the Pareto dominant solutions is called Pareto Front, and it could be visualized as a line, a surface, or a hypersurface depending on the number of the objectives. In this case,17 the Pareto Front will correspond to a line since the objective in this work are two. In the best case for traditional optimization, one obtains a set of Pareto-optimal solutions that have the following properties: - They are near to the real Pareto Front - They are evenly distributed along the real Pareto Front

- Many extreme solutions are included. As in single objective optimization process, in EMO the criterion for evaluating the fitness of a solution play a relevant role. In this work a Pareto - based method will be used calculating the fitness for every individual in the generation in this way rating the entire population and subsequently applying the genetic operators such as elitism, heredity and cross over. 18

A

Pareto - Front

Objective 1

Objective 1

Left: Definition of dominated solutions. Right: Pareto - Front.

17 Robert Vierlinger, 2013, “Multi-Objective Design Interface�, University of Applied Art Vienna, Vienna, pp. 31-36 18 Ibidem.

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3.1 Sydney Analysis

Sydney is the most populated city in Australia and Oceania and the capital of New South Wales state. It is able to attract an incredible amount of people from all around the world thanks to its strong economy and its high quality living standards. More than 5.000.000 people are now living in the area withe the 36% of them born overseas (65% if local residents with at least one migrantborn parent is included). Those facts are the confirmation of how much Sydney is a multicultural and vivid city.1 Sydney metropolitan area sprawls from the Blue Mountain on west to the Pacific Ocean. Its natural harbour made this area the most suitable for the first settlement of the British settlers. Thanks to its strategic position the city grew fast around the harbour while at the

same time the absence of other urban areas led to an uncontrolled urban sprawl. The metropolitan area today is more than 12.000 km2 with 658 different suburbs and 40 local governments.2 Like other recent settlements, Sydney had the opportunity to spread horizontally while concentrating the verticality only in few and relative small areas. The City Business District (CBD) is the heart of the economic power of the city. The area is mainly used for offices in fact only 17.000 people are sharing the 2,7 km2 of the CBD reaching the density of 6,160 people/km2. Hence, the majority of the people are living in the suburbs areas leading to a lowering the quality of several urban services such as transports, schools, hospitals etc.3

1 Australian Bureau of Statistics - Census - http://www.abs.gov.au/ 2 Sydney - Wikipedia - https://en.wikipedia.org/wiki/Sydney 3 Ibidem.

Sydney Opera House at night, Sydeny, 2016

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Greater Sydney

City Business District City of Sydney Population Density

Census 2006 [persons/ha] < 10

International Airport

10 - 20 20 - 30 30 -35 35 -40 40 -45

Metropolitan Area

45 - 50 > 50

Sydney Metropolitan Area.

The aim of this work is to propose a new way of conceiving the urban area of global cities like Sydney. The new and massive flow of people that are arriving could risk to enlarge the suburbs increasing social inequalities, decreasing urban functionalities and reducing architectural quality in order to achieve an affordable prize for the new developments. Compared to other metropolitan areas, while the

Population Density of Sydeny Metropolitan Area - Census 20065

Population population is close to the average the density level is incredible low. 2 mln This situation is affecting the citySYD NYC public transport and moreover is incrementing inequality as the 4 distance with the centre increase. TKY SGP The poor quality of the public transport due to the city size and the necessity of mobility to reach LND SEL the business district is inducing even more people to use the car as Sydney population (2016): 5.029.768 a main transport system.

Density [ppl/km2] +1600 Km2

SYD

2 mln

NYC

SYD

NYC

TKY

SGP

TKY

SGP 36,8% 3 bedroom

LND

SEL

Sydney density (2015): 400/Km²

SYD 24,9 % 2 bedrooms

6,6% 1 bedroom

Compared city density.6

LND

TKY

29,0 % 4+ bedrooms

SEL

LND

Average of bedroom per dwelling: 3 SydneyN°population (2016): 5.029.768

Compared city population.7

5 Australian Bureau of Statistics - Census - http://www.abs.gov.au/ 6 Wikipedia City Pages - https://en.wikipedia.org 7 Ibidem.

4 Australian Bureau of Statistics - Census - http://www.abs.gov.au/

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Population House Layout

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Syd


Berowra

Method of Travel to Work Total workers: 1,654,042 72,6 % Car 11,5 % Train 6,5 % Bus 5,1 % Walk 2,6 % Bycicle 1,2 % Other

Circular Quay Olimpic Park Wynyard Central Bondi Junction

Method of travel to work in Sydeny.9

International Airport

Liverpool

Projections 200,000

400,000 315,200

300,000

Cronulla

212,550

200,000

Campbelltown

100,000

2001 2006 2011

2001 2006 2011

2021 2026 2031 2016

Sydney’s transport system is After the 2015 Census is mainly based on trains, which, due becoming clear the necessity to to the very large area to cover, are reinvent the city if it wants to tackle sometimes inefficient and slow. On the issues of the contemporary contrary the transportation systems society. The population will almost Density [ppl/km2] Population Densitythe [ppl/km2] works perfectly in the area of the Population double in 15 years while CBD City Business District where the will face a growth of the 50% of 2 mln +1600 Km2 2 mln +1600 Km2 density is higher and the public This that SYDNYC its population. NYC NYC SYD SYDmeans SYD NYC system is able to satisfy more the city will tent to spread even people simultaneously. According more to accommodate the future to a recent survey (Census 2015) the inhabitants. For these reasons, this TKY SGP TKY TKY TKY SGP SGP 72% of the population is using the work hopes to push the discourse car as preferred method to travel to on a denser city further taking care work. With the growing size of the not only of the urban problem but LNDSEL SEL LND LND qualityLND SEL urban population, this could affect also ofSEL the architectural and dramatically the environment and aesthetic one.8 Sydney population (2016): Sydney Sydney population (2016): Sydney density (2015):density (2015): 5.029.768 400/Km² the usability of the city itself. 5.029.768 400/Km²

315,200

50,000

100,000

Sydney Train Transport System.

150,000

2021 2026 2031 2016

2036

Population Projection for CBD Area

2036

Dwellings Projection for CBD Area

Population and Dwellings projections in the CBD area.10

House Layout

6,6% 1 bedroom

6,6% 1 bedroom

House typologies House typologies

House Layout

24,9 % 2 bedrooms

24,9 % 2 bedrooms

12,8 % 12,8 % Terrace or Townhouse Terrace or Townhouse

SGP

60,9 % 60,9 % Separate House Separate House 36,8% 3 bedroom

36,8% 3 bedroom

29,0 % 4+ bedrooms

29,0 % 4+ bedrooms

N°per of bedroom Average N° of Average bedroom dwelling: per 3 dwelling: 3

25,8 % Flat or Apart-

25,8 % Flat or Apart-

N° of people per Average N° of Average people per household: 2.7household: 2.7

House Layouts and Typology in Sydney.11

8 Australian Bureau of Statistics - Census - http://www.abs.gov.au/

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9 Australian Bureau of Statistics - Census - http://www.abs.gov.au/ 10 Ibidem. 11 Ibidem.

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3.2 Barangaroo

The site of the project is the Barangaroo suburbs It is locate in the inner city and adjacent to the CBD. Historically the area was an important settlement for the Aboriginal Cadigal people as a fishing and hunting place. With the arrival of the British settlers, the area changed its name temporarily with East Darling Harbour until 2006. Barangaroo is located in a strategic position. It could be considered the continuation of the Darling Harbour area and at the same time, it is relatively close to Circular Quay and the Harbour Bridge. Although its proximity to the city, Barangaroo still preserve its natural and uncontaminated soul also thanks to the reserve and Millers Point.

Aborigenals celebrating at Yabun Festival - Sydeny - 2017

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Several transport systems are serving the area. A new ferry wharf is supposed to connect the site with Circular Quay, Paramatta River on west and all the bay area on east. A train system is connecting Barangaroo with the City Circle and with Central Station even though the closes station is Wynard. With the new urban development of 2012, a new train station will be located in the heart of Barangaroo making the transportation of workers, tourist and inhabitants easier. In conclusion, the main highway linking North Sydney with the Inner City is just passing tangential to the site. Barangaroo has a great potentiality to become an important city hub for Sydney and to represent a new way of perceiving the city of the future.

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Sydney CBD - Transports and points of interest

Natural Bay

Manly/Taroonga zoo

Paramatta

Harbour Bridge

North Sydney

Barangaroo Reserve

Circular Quay

Sydney Harbour

Circular Quay Train Station

Site - Barangaroo

Wynyard Train Station Sydney Tower

Direction Central

Darling Harbour

Ferry Routes Highways

Point of interest Train Station Baraganroo

Aerial View of Barangaroo construction site.

In 2009, Barangaroo Delivery Authority established an urban and architectural competition for the renewal of the area. In 2012 the masterplan and the architectural design of the area was released. The development is dividing the area in three precincts: South Barangaroo, Central Barangaroo and The Barangaroo Reserve. The project is planning to provide mainly offices but also some mixed used buildings and an hotel to There have been several criticisms of the urban development plan especially it was questioned the real necessity for a luxury hotel. 12

This work is challenging the essentiality of a luxury tower while trying to provide answers to the low-density problem of Sydney with the new mass of people coming every year. Optima tower will replace the hotel while not changing the masterplan. In the next page, it will be shown the new urban development plan and other environmental analysis about the tower site such as the radiation analysis, the wind roses and a general flow analysis.

CBD area - Transport System - Point of interests - Site location.

12

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Barangaroo Development Official website - http://www.barangaroo.com/

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Barangaroo Area - Access and Routes

Paramatta

Barangaroo Area - Uses

Circular Quay

Barangaroo Reserve

Barangaroo Reserve

Central Barangaroo

Central Barangaroo

Site

Site

South Barangaroo Cars

South Barangaroo

Pedestrian

Retails Residential

Routes

Office

Panoramic Path 0

Mixed Use

50 25

Historic

100

Darling Harbour

0

50 25

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100

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Barangaroo Area - Radiation Analysis 1/2

Barangaroo Area - Radiation Analysis 2/2

kWh/m2 2.25<= 2.16 2.07 1.98 1.89 1.80 1.71 1.62 1.53 1.44 <= 1.35

Radiation Analysis Sydney - NSW - Aus 21 Jan 9:00 - 21 Jan 16:00

kWh/m2 2.42<= 2.20 1.99 1.77 1.56 1.35 1.13 0.92 0.70 0.49 <= 0.27

Radiation Analysis Sydney - NSW - Aus 21 Jul 9:00 - 21 Jul 16:00

kWh/m2 2.35<= 2.19 2.02 1.86 1.70 1.53 1.37 1.21 1.04 0.88 <= 0.72

Radiation Analysis Sydney - NSW - Aus 21 Apr 9:00 - 21 Apr 16:00

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kWh/m2 3.57<= 3.34 3.12 2.89 2.67 2.44 2.22 1.99 1.76 1.54 <= 1.31

Radiation Analysis Sydney - NSW - Aus 21 Oct 9:00 - 21 Oct 16:00

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Wind Rose Analysis

January

February

E

N

W

March E

S

April

Flow Analysis in three period of the day

N

E

S N

May

S

W

W

June

E

E

Time Period: 8 - 10

E

Tourists - Leisure Flow Workers Flow

S N

N

July

W

SN

August

E

W

S

September

W E

E

Time Period: 12 - 14 N

October

SN

S N

W

November

W

S N

N

December

E

E

Tourists - Leisure Flow

S

Workers Flow

W E

SN

S

Time Period: 18 - 20 Tourists - Leisure Flow Workers Flow

W

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W

W

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Maxium GFA (Theoretically): 311,190 m2 Floor to Floor height: 4 m Floor Plate Size: 4510 m2

14 m

3.2.1 Building Regulation and SEPP 65

Sydney zoning plan describes the uses and the height of the Barangaroo area. Within the development plan, the chosen site is the biggest with a gross floor area of 79,665 m2. Moreover, the urban plan for that area prescribes a height of 275 m hence the highest potential building in the Barangaroo area.13

Furthermore, in 2005 planning authorities in Sydney drew up a guideline book (SEPP 65) in which are presented all the prescriptions for building architectural quality.14 The SEPP 65 gives suggestions for designing quality apartments regarding solar access, natural cross ventilation, building layout etc.

Maxium GFA (Theoretically): 386,910 m2 Floor to Floor height: 4 m Floor Plate Size: 5607 m2

Maxium GFA (Theoretically): 311,190 m2 Floor to Floor height: 4 m Floor Plate Size: 4510 m2

14 m

Gross Floor Area: 79,665 m2

Land Zoning: B4 (Mixed Use)

Maxiumum Height: 275 m

SEPP 65 Guidelines

Sunlight Access 70%

Apartment Layout

of the apartment should recieve 2h of direct sunlight

Very Good

Good

x

x

Fair

Ok

x

m2

studio

1 bedroom

2 bedroom

3 bedroom

35 m2

50 m2

70 m2

90 m2

>10 Storeys 1 Core for every 40 apt.

1/2 x 1/2 x

13 Department of Planning & Environment of NSW - http://www.planning.nsw.gov.au. 14 SEPP 65 http://www.planning.nsw.gov.au/Policy-and-Legislation/Housing/~/ media/7ED8E40113064120AEE3432457390171.ashx

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One important prescription that drove the design process of this work is the access of direct sunlight. In the guideline a 2 hours of direct sunlight for the 70% of apartment is suggested. This fact influenced the following criteria for the implementation of the Genetic Algorithm.

Another important instruction refers to the apartment layout. In fact, the SEPP 65 gives reccomendation corcerning the minimum size of the apartments. E.g. A studio should not GFA (Theoretically): 386,910 beMaxium smaller than 35 m2.m2Thi last idea Floor to Floor height: 4 m Floor Plate Size: 5607 m2 has been considered during the design process affecting the choise of the size of the initial voxels.15

15 SEPP 65 http://www.planning.nsw.gov.au/Policy-and-Legislation/Housing/~/ media/7ED8E40113064120AEE3432457390171.ashx

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3.3 GA Implementation

The form finding process of this work is mainly based on the implementation of Genetic Algorithm in order to solve a Multi Objective Optimization problem. For the of high-rise buildings typology, two factors result to be crucial in the design process. First of all, due to the building size and weight, it is important to address properly structural engineering design issues. The self-weight of the structure, the strong wind forces affecting especially the upper parts and the high number of floors are key factors to take into account during the design process. At the same time, a more efficient and dense structural system could affect negatively the solar access to the floor plates. Structural fitness and solar access are the two objective that this work is trying to optimize.

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Since its typological complexity, the effectiveness of the Genetic Algorithm it is strongly dependent with the accurate choice of the fitness functions and the gene pool. Problems with vast complexity if not properly simplified and defined will almost certainly results in frustration.1 Using the GA this research on one hand, it will try to maximize the solar access for the building floors according to the building regulations constraints and on the other hand, it will minimize the structural displacement of the steel elements. At the end of the iteration process, the last 2 generations will be selected to search for the final results which is preferred by the designer.

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3.3.1 Genomes

Possible Configurations 4m

4m

275 m

The choice of the gene pool is one of the two aspects, which can influence drastically the final outcomes. The idea in this case is to voxelize the total buildable building mass and later, those voxels will be eroded in order to maximize (or minimize) the fitness functions. The total voxels population will

cover plot size entirely expect for the setback area of 14 meters prescribed by the local building regulations. The total mass will be 110 m wide, 56 m depth and 275 m tall. Counting that the process will be erosive, the final outcome will result in a slimmer and thinner volume.

32 m2

48 m2

80 m2

64 m2

48 m2

80 m2

64 m2

48 m2

96 m2

64 m2

80 m2

96 m2

Front View 110 m

56 m

Top View

Left: Voxels Cloud Top and Front view. Right: Possible Configuration

The 4 m3 sized voxel is thought for possible aggrupation and future plan layouts. The minimum size of a group will be 32 m2 which correspond to the minimum size requirement for a studio according to the SEPP 65 guidelines. The erosion process will be performed using an agent - based behaviour. The starting point of the agents is located on the top curve of the building bulk, which is scaled and tapered in order to facilitate the process. The culling points will start their path from the upper curve arriving to the base curve. At the same time two other curves

will be place inside the volume representing the possible location of the cores. Due to its big size, the agents will follow both the initial both the scaled perimeter of the solids. In this way the system will try to produce two different masses. The two concentric solids are inversely tapered. The bigger one shrinks for the top to the bottom while the smaller one on the other way round. Therefore, the erosion will be predominant on the upper part. The main behaviour of the agents is to reach the target curve (on the bottom) avoiding the core

Voxels Cloud

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lines. In doing so to every agent will correspond a specific location on the top curves and a specific target on the bottom curves. The location of the starting and target points of the agent with core lines one will represent part of the gene pool that will be used for the Genetic Algorithm. The other two genome sets are the starting angles of each culling point and the location of the two core lines. The number of the culling points is based on the length of the curve which they belong. The bigger curve will have 18 agents while the inner one 9. The culling space for a

Erosion of the floorplate

single agent is 27 representing the number of voxels eroded for every floor. The gene pool is therefore made of 4 sets: the coordinate of the agents starting points on upper curves and the relative targets at the bottom curves, the starting angle of the path and the location of the building core to avoid. Every gene set is constrained according to its boundary condition. The starting points and the targets have to lie on the relative curves, the angle has to be included in the domain [0 180] and the core curves have to be inside the bottom curve boundary.

POINT CLOUD FOR FLOORPLATE: CULLING SPACE: CULLING PTS (TOT.):

320 10 27

N° of Culling Pts based on the length

Culling space

Starting Curves

0

X

Genome Set_1

Starting Curves

Genome Set_2

Coordinates of agents starting point on upper curves

Coordinates of agents target points on bottom curves

Genome Set_3

Genome Set_4

Target Curves

μ Inner Curves

Internal Solid

Outer Curves

External Solid

Target Curves 0

Starting angle and velocity

X’

Cores

Coordinates of thecenter of the cores (to avoid)

Gene pool

Starting and target curves.

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Iteration 10

Iteration 20

Iteration 30

Iteration 50

During their path the agents have their own behaviour based on local conditions. The desired velocity represent the amplitude and the direction of the vector linking the starting with the target. The steering force instead is affecting the straight path of the points with a certain intensity. Furthermore they will have a Cohesion Behaviour and an Avoidance one. The first one is influencing the ability of the system to average the path mutually. When five agents are close enough to each other they start using their averaged motion vector. For this

reason after few iteration bundles start to emerge making clear the principal direction for the erosion. At the same time the Avoidance is forbidding the system to get too close to the core curves. The logic is applied for every iteration, computing the initial velocity, the desired velocity and the neighbours behaviour using the Cohesion and the Avoidance Forces.

Steering Force

Cohesion Behaviour

Avoidance

Iteration 60

Iteration 80

DESIRED vELOCITY: MAX SPEED: COHESION SPACE: COHESION RATE: STEERING FORCE: Iteration 90

18 6 5 0.2 2.2

Iteration 100

Iterations of the agent-based behaviour

95

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3.3.2 Phenotype Convex Hull

The Phenotype could be considered as the physical representation of a specific genome sets. The location of the starting and target points on the curves, the two cores lines and the starting angles will determine a specific initial state of the agent-based behaviour which consequently is eroding the voxels

mass generating the floor plates. The phenotype will be the group of floor plates, created using a convex hull algorithm, and a basic structural system. The floorplates areas will be used to analyse the first fitness function that provides the number of direct sunlight hours.

Agents - Remaining Voxels - Floorplates.

97

Even though the structural frame is not the definitive one, it could be used to compare the different phenotypes. The displacement of the structure will be the second objective of the optimization process.

Base Structural System

Example of Phenotype

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3.3.3 Fitness Functions

The first fitness function that will be analysed is the structural displacement. The loads applied to the phenotype structural systems are: wind loads (from a wind analysis), self weight of the structure and the live and dead loads for a mixed used building. The displacement of the structure will

Initial Structure

min [cm]

99

represent the first fitness value. The second fitness function is the average sunlight hours access for every 2 m2 of the floor plates during the darkest day in Sydney. According to the local building regulation a good result is 2 hours of sunlight for every apartment.

Deformation Structure

max [cm]

Fitness Function 1: Structural Displacement

Live/Dead Loads

Wind Loads

Self Weight Load

Fitness Function 2: Sunlight Hours

Context

Sun Path

22 Jul 08:00-16:00

Compared

min [h]

max min [h] [cm]

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min [h]

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max [h]

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3.3.4 Generations

Generation_0

Generation_1 Population size: 100

Population size: 100

Mutation

The multi-optimization algorithm is generating a phenotype for every gene pool. The group of phenotypes is called generation. Every individual of the generation is then evaluated according to the two fitness functions. After the evaluation process, some of the individual survive other die based on

Mut Rate: 0,400 Mut Probability: 0,600

their fitness values. The survivals are then subjected to the evolutionary operators such as Crossover, Mutation, Mating and Breeding. For this work it has been adopted a mutation rate of 0.400 and a mutation probability of 0.600 which means that 4 individual out of 10 could be subjected to

CrossOver

Crossover rate: 0,400

Elite

Elitsm: 0,400

Genomes

Fitness Funcion _1

Highest Value of Fitness (Elite) Lowest Value of Fitness

Loop the process for every generation

Genetic Algorithm Operators

Phenotype

Fitness Funcion _2

Loop untill it reaches a very good valuefor the Fitness Functions

General mechanism of the Genetic Algorithm

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the mutation with a probability of the 60%. At the same time the 40% of the individual in a specific generation will be more suitable to the crossover. Furthermore the 40% with an higher value of fitness compered to the other individuals of the same generation will survive for the follow one. Those values have been specified according to the problem which this work is dealing with. A relatively high level

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of mutation rate and probability will try to overcome the hill climbing problem. In the meantime a value of elitism lower than 60% will affect negatively the computational time of the algorithm while aiming for a better solution. The algorithm will compute until the generation 50 which is a good approximation of the solution of the problem. The following pages will show the last 2 generation.

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Generation 48

f1: 12,61

f2: 20,46

f1: 1,318

f2: 31,92

f1: 1,314

f2: 44,31

f1: 1,377

f2: 1,507

f1: 1,229

f2: 1,994

f1: 1,272

f2: 1,755

f1: 1,436

f2: 16,07

f1: 1,376

f2: 33,84

f1: 1,425

f2: 14,11

f1: 1,343

f2: 1,260

f1: 1,308

f2: 1,414

f1: 1,490

f2: 1,732

f2: 10,61

f1: 1,176

f2: 1,367

f1: 1,103

f2: 1,326

f1: 1,393

f2: 0,990

f1: 1,207

f2: 1,072

f1: 1,066

f2: 1,078

f1: 1,208

f1: 1,216

f1: 1,411

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Generation 48

f2: 12,65

f2: 12,56

f1: 1,214

f2: 24,31

f1: 1,171

f1: 1,376

f2: 19,72

f1: 1,215

f2: 16,27

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f2: 1,627

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Generation 49

f1: 1,431

f1: 1,384

f1: 1,280

f1: 1,644

105

f2: 1,226

f2: 0,904

f2: 0,998

f2: 1,609

Generation 49

f1: 1,353

f2: 1,198

f1: 1,069

f2: 1,561

f1: 1,447

f1: 1,376

f2: 1,345

f1: 1,442

f2: 0,908

f1: 1,491

f1: 1,335

f1: 1,341

f2: 1,250

f2: 1,204

f2: 1,391

f2: 1,332

f1: 1,281

f2: 0,959

f1: 1,407

f2: 1,384

f1: 1,444

f2: 1,060

f1: 1,250

f2: 1,278

f2: 1,338

f1: 1,384

f2: 0,940

f1: 1,393

f2: 0,991

f1: 1,186

f2: 1,202

f1: 1,279

f1: 1,249

f2: 1,160

f1: 1,286

f2: 1,263

f1: 1,226

f2: 1,332

f1: 1,383

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f2: 0,953

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Generation 50

f1: 1,276

f1: 1,514

f1: 1,403

f1: 1,368

107

f2: 1,726

f2: 1,012

f2: 1,229

f2: 0,907

Generation 50

f1: 1,435

f2: 1,386

f1: 1,514

f2: 1,201

f1: 1,532

f2: 1,203

f1: 1,105

f2: 1,615

f1: 1,389

f2: 1,398

f1: 1,373

f2: 1,212

f1: 1,399

f2: 1,011

f1: 1,225

f2: 1,210

f1: 1,401

f2: 1,522

f1: 1,326

f2: 1,437

f2: 0,913

f1: 1,452

f1: 1,389

f2: 1,360

f2: 0,952

f1: 1,385

f1: 1,510

f2: 1,050

f2: 1,965

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f1: 1,355

f2: 1,010

f1: 1,310

f2: 1,235

f1: 1,484

f1: 1,431

f2: 0,905

f1: 1,165

f2: 1,720

f1: 1,408

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f2: 1,418

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Generation 50

f1: 1,554

f1: 1,484

f1: 1,314

f1: 1,417

109

f2: 0,992

f2: 0,973

f2: 0,896

f2: 1,303

Generation 50

f1: 1,235

f1: 1,39

f1: 1,486

f1: 1,453

f2: 1,481

f2: 1,069

f2: 1,062

f2: 1,113

f1: 1,460

f2: 1,768

f1: 1,273

f2: 1,195

f1: 1,309

f2: 1,347

f1: 1,244

f1: 1,455

f2: 1,112

f1: 1,321

f2: 1,215

f1: 1,398

f2: 1,290

f1: 1,521

f2: 1,037

f1: 1,398

f2: 1,009

f1: 1,462

f2: 1,175

f1: 1,721

f2: 1,104

f1: 1,377

f2: 1,005

f1: 1,450

f2: 1,072

f1: 1,505

f2: 1,967

f1: 1,422

f1: 1,317

f2: 1,294

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f2: 1,300

f2: 0,987

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3.3.5 Results

The graph below is showing the non Pareto dominat solutions of the generation 50 and 45. After 45 generations the fitness values of the individuals are minimized. The last generation is a little bit closer than the previous and 5 individuals are defining the Pareto front. In the fig below, it is shown how the fitness values are changing

during the evolutionary process. The values that the graph are showing is the average fitness for every generation. The trend of the curve is not completely decreasing (or increasing for the sunlight hours). But the difference between the first generations and the last one is noticeable

Displacement [cm]

Sunlight Access [h]

2,50 2,34

1,50 1,44

2,18 2,02

1,38 1,32

1,86 1,70 1,54

1,26 1,20 1,14 1,08

1,38

1,02

1,22

0,96

1,06

0,90

0,90 17

20 22

25 27

30 32

35 37

40 42

(cm) Fitness 2

Displacement

Displacement [cm]

Sunlight Access [h] 1,50 1,44

2,18 2,02

1,38 1,32

1,86 1,70 1,54

1,26 1,20 1,14

0,96

1,06

0,90

0,90 17

Gen: 45 Pareto Front Pareto Front.

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Solar Access (h) Fitness 1

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20 22

1,02

1,22

Elitism: 0.400 Mut. Probability: 0.600 Mutation Rate: 0.400 Crossover Rate: 0.400 Population Size: 100

17

1,08

1,38

Gen: 50

50

Generation

First fitness function during generations.

2,50 2,34

45 47

20 22

25 27

30 32

35 37

40 42

45 47

50

17

20 22

25 27

Generation Second fitness function during generations.

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30 32

35 37

40 42

45

47

50

Generation

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25 27

3


The phenotype presented in the early pages represent the elite for the relative generations. E ach of those individuals is theoreti cally suitable to be choosen. In fact they belongs to the last 3 generation where a fair approximazion of the solution is reached. During the first generations the

building is more compact. This trait is slowly fading away with the next generation in favour of a more slim shape with a bridge or two separate towers (fig below). On the opposite page is presented the phenotype selected for the design of the skyscraper.

Final Phenotype

Solar Analysis for the Final Phenotype

Top: Principal outcomes of the MOO process. Bottom: Solutions considered not valid.

Structural Analysis for the Final Phenotype.

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3.4 Structural Aggregation

The chosen phenotype still represent a bumpy solution for the skyscraper design. Surely, it has a better structural fitness value compare to the other solution of the previous generations. The next step in the design process is to improve the structural efficiency of the phenotype redesigning the structural frame. In doing so, the floor plates will be rounded achieving a more organic shape and avoiding swings that could cause a loss of the overall structural stability.

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Furthermore, the new structural grid is thought as a self-organizing system. In fact, the frame will selfarrange in order to provide more stiffness where the tension is higher openings in some area of the building facade. In this way, the structure will create many possibilities in the light of entering. The final goal is to achieve a system with a dual objective: improve the stability of the structure and facilitate the natural lightening of the building.

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3.4.1 Structural Analysis-Organization

Loads

Elements

The first step involve the reconstruction of a unified mesh coherent with the arrangement of the structural floor plates. For this purpose, a marching cube algorithm is used and the following result is refined. Subsequently the mesh is subjected to a Finite Element

Analysis during which the areas under compression and tension are highlighted in white and black respectively. The points in which the highest value of stress are recorded are marked and they will be the points where the structural frame will thicken.

Supports

Phenotype

Fem Analysis

Materials

Load Cases Wind Loads Gravity Loads Dead / Live Loads

Load Cases

Load Cases

Loads Utilization of Wind the mesh

Wind Loads

0%Gravity Loads 100%

-100%

Gravity Loads

Dead / Live Loads Compression Utilization

Dead / Live Loads

Tension Utilization

Utilization of the mesh 0%

-100% Compression Utilization

Utilization of the mesh -100% Compression Utilization

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0%

100% Tension Utilization

100% Tension Utilization

Density Points 80 - 100%

80 - 100%

Points of the utilization mesh that have a value between 80 - 100 % in both compression and tension

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The new structural system will follow four categories of behaviour. At the beggining every node of the frame will look for the closest density point. The first action will be the movement toward the point on the base plane scaled by a constant [K1]. In this way the node belonging to a certain floor plate will remain at the same z coordinate. The second group of behaviour is referred to the topology constrains.

In fact, during the self organization process the structure will try to avoid interpenetration between the element. When two node are getting close of a certain range a repulsion force is applied [K2]. With the same logic the constant [K3] forbid the extreme stretching of the elements. Finally, the last constant [K4] is for bypass the problem of structural cusps which could emerge during the process.

Constant K_1

|V|

|V| Find the closest

Project the vector on the XY plane

|V| Remap vector lenght based on the distance

Amplitude of the vector: constant.

K_1

Constant K_2 3 1-3 1

2

1-2

Find the closest on the plane

1-3

3

1

3

If |1-3| < RangeK_2

1-3

1

1-3

3

Apply repulsion force

1

Amplitude of the force: constant

K_2

Constant K_3

3

3

K_1

Topology Constrains

Build the topology

-3

1

-3

1

-4

Move towards the Dense Point

1-5

1

1 5

Find The Closest Point

3

-3

4

|V|

1-3

3

1

1-2

2

Connection Lenght

1

1

If |1-3| > RangeK_3

1

Apply attraction force

K_2 K_3 K_4

Amplitude of the force: constant.

K_3

Constant K_4 2

|V|

3

1

ve towards the Dense Point

Topology Constrains

Build the topology

5

119

3

1

4

Find the connected nodes

K_2 K_3 K_4

2

5

2

3

1

4

Project them on the plane

5

2

3

1

4

Average point of the porjections.

5

4

Amplitude of the force: constant.

K_4

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121

Iteration 0

Iteration 3

Iteration 6

Iteration 9

Iteration 12

Iteration 15

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Final Result of the structural aggregation

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3.5 Skin

So far, the wrapper mesh has been used to inform the Genetic Algorithm and subsequently the structural system during its aggregation process. Generating the building skin this project aim to create a system congruent with the structure and with the initial mesh. In doing so the same topology of the structural system will be used so every triangular panel of the skin will be surrounded and supported by the relative three element of the structure. The skin design process is not only a driven by topological and constructability issues. The idea is to produce a skin able to adapt to the environmental boundary condition. The basic logic is to close

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every triangular panel on the relative side where the radiation incident is higher. Moreover using the structural mesh topology the two separated elements of the triangular face will share two of their sides. Finally, the resulting skin will both provide a better amenities for the internal activities both will inform the program. In fact, where the openings are bigger activities with a higher requirement of light will take place. On contrary, activities with no needs for extensive light will be placed in areas with surrounded by small panels. Concluding, the initial mesh not only informed the structural aggregation but also the building skin and hence the skyscraper program and layouts.

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Radiation Analysis 1 / Dic / 08:00 - 18:00

Radiation Analysis 1 / Dic / 08:00 - 18:00

0 kWh/m2

2.01 kWh/m2

0 kWh/m2

2.01 kWh/m2

The initial step was to run a radiation analysis on the mesh. In this way, the area with an high value of radiation will be underlined. Consecutively, for technical issue the coloured radiation mesh is remapped between the black and white colours. A value, hence a colour, is applied to every vertex of the panels. Adjacent vertices will share the same colour, ensuring the continuity of the skin pattern. A weighted average point is then calculated on the panels sides. The point on the horizontal side will be the starting point of the principal direction of the pattern while the other two will

be used as starting points of the secondary direction. The first rule is to move the point on the horizontal side perpendicularly until it reach one of the two other sides. When it is close enough (according to a range), it reach the opposite vertex. The result is a panel divided in two parts. Afterwards, the other two starting point will follow the same rule avoiding to intersect the first direction. The two polygons represent the openings, while the other two lines will be used as a lighting system energetically independent, adding a landmark value to the whole building.

Principal Direction

0 Vertex Color_0: 107,107,107

y

Vertex Color_1: 23,23,23 Vertex Color_2: 230,230,230 2

x

1 Remaped between 0.00 - 1.00

Horizontal Edge: starting line Vector: Normal Direction

0

2

1

Weighted average point_0 Weighted average point_1 Weighted average point_2

X: Treshold Distance 0.6 m Vector: Tangent of the closest curve

Y: Treshold Distance 1.0 m Vector: two points connection

Secondary Directions e2

0

e2 x2

Normal Vector Tangent Vector 2

Radiation Mesh Analysis

125

1

e1

x1

Binormal = Normal Face

Environmental and topological analysis

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e1

Obliques Edges: Secondary directions Vector: Normal Direction

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x1, x2: Treshold distance 0.4m Vector: Tg of the Principal Curve

Final Configuration of the curves

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127

Iteration 10

Iteration 20

Iteration 30

Iteration 40

Iteration 10

Iteration 20

Iteration 30

Iteration 40

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Circulation System

129

Floorplates

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Structural System

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Skin

130


3.6 Design Project

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Uses

Hours 12

Services Car Park

Residential 19.98% 14.000 m2

1

Amenities

2

Hall Balcony

3

Storage

4

Meeting Room

5

Cafe

Private

58.08%

Office 21.20% 15.000 m2

6

Car Park

7

Balcony Hall

8

Services

9

Gym

10

Pool

Hotel 16.90% 12.000 m2

Balcony

11

Bar

12

Services

1

Restaurant

Retail 17.10% 12.000 m2

Storage

2

Shops

3

Grocer

4

Services

5

Pub Food Court

Public

41.92%

6

Restaurant

Leisure 16.70% 11.300 m2

Green areas 8.23% 5.250 m2

7

Bar Gallery

8

Gym Pool

9

Theatre

10

Plaza

11

Terraces

Uses and Hours Diagram

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21%

Residential

24 %

Office 9

5277 m2

Hotel Retail Leisure

Conectivity Privacy

n

Common Spaces

View

Poor

Fair

Good

10114 m2

12

6115 m2

7

7504 m2

4

9436 m2

4

4234 m2

8

8975 m2

5

12348 m2

400 m2

27 % 9%

Excellent

Elevators Core

Floor Plate Size 12

19 %

2834 m2

3995 m2

8

2992 m2

5

6284 m2

12

Escaletors

Feature of the floorplates compared to their size.

135

Prospective of the program

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Activity Colour Rs

Residential Office Hotel Leisure Retail Common Spaces

Cf Br

Gy Pl

Pu

Public Circulation Office Circulation Residential Circulation Hotel Circulation Pl

Sr Cf

Node Type

B

Br

Cf

Foyer / Corridor Direct Access One Way Access Escaletor

Mt Cf

Gy

Rs

Cf

Sh Gr Pz

Tr

Am Br

B

Mt

Br

Rs

Sh

Cf

Pl

Th

Gr

Th

Sr

Fc H

Ba

Gr Tr

Gy

Pz

Ga Rs

Pu

Sh

St

Cf

Private

Am

Amenities

Gr

Grocery

Sh

Shop

B

Balcony

H

Hall

Sr

Services

Public

Ba

Bar

Mt

Meeting Room

St

Storage

Outside

Br

Br

Pl

Pool

Th

Theatre

Cafe

Pu

Pub

Group

Cf

Tr

Terrace

Fc

Food Court

Pz

Plaza

Ga

Gallery

Rs

Restaurant

Sub Activity

Circulation concept diagram

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H

Circulation Diagram

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9.2

10 .5

16.2

85.8

4.5

37.0

Shop 108 m2

42

.3

Restaurant 142 m2

23.4

Reception 222 m2

25

.0

1

Second Tower Foyer 230 m2

Toilette 1 58 m2

Core 1 186 m2

22.4

2.4

Restaurant 145 m2

1 Market 380 m2

Core 2 84 m2

Toilette 2 58 m2

+ 0.50

Food Court 250 m2

5 0

20 10

12.6

17.5

30

6.9

27.3

10.5

7.0

12.1

+ 0.00

9.6

86.4

Ground Floor Plan

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- 3,80

28.9

14.1

16.2

12.8

1 Restrooms 56,66 m2

11.3

12.7

15.3

Unit 1 149,16 m2

Office Area 1192 m2

Unit 4 88,98 m2

Kitchen Area 53,38 m2

+114,80

29.8

30.8

Conference Areas 70,60 m2

+114,80

19.5

12.4

1

Unit 2 172,75 m2

Unit 3 151,11 m2

13.9

39.3

Core 2 84 m2

+ 9,60 +15,10

6.7

33.2 12.5

14.3

11.8

16.5

16.2

28.3

42.6

+ 0.00

5 0

20 10

30

23th Floor Plan

141

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+268,30

+ 254,00 + 249,20

+ 240,00 + 235,20 + 230,40 + 225,60 + 220,80 + 216,00 + 211,20 + 206,40 + 201,60 + 196,80 + 192,00 + 187,60 + 183,20 + 178,80 + 174,40 + 170,00

+ 169,10

+ 165,60 + 161,20

+ 161,20

+ 156,80

+ 156,80

+ 152,60

+ 152,60

+ 148,40

+ 148,40

+ 144,20

+ 144,20

+ 140,00

+ 140,00

+ 135,80

+ 135,80

+ 131,60

+ 131,60

+ 127,40

+ 127,40

+ 123,20

+ 123,20

+ 119,00

+ 119,00

+ 114,80

+ 114,80

+ 110,60

+ 110,60

+ 105,80

+ 105,80

+ 101,60

+ 101,60

+ 96,80

+ 96,80

+ 92,00

+ 92,00

+ 87,20

+ 87,20

+ 82,40

+ 82,40

+ 77,60

+ 77,60

+ 72,80

+ 72,80

+ 68,00

+ 68,00

+ 63,20

+ 63,20

+ 58,40

+ 58,40

+ 53,60

+ 53,60

+ 48.80

+ 48.80

+ 44,00

+ 44,00

+ 39,20

+ 39,20

+ 34,40

+ 34,40

+ 29.60

+ 29.60

+ 24.80

+ 24.80

+ 20.00

+ 20.00

+15.10

+15.10

+ 9.60

+ 9.60

+4.80 + 0.00

+ 0.00

- 5,20 5

- 9,50

0

20 10

30

- 14,30

Section A-A 143

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View of the tower to the Sydney Bridge 145

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146


View from the water 147

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Close up view 149

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Night View 151

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Interior View 153

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3.7 Physical Model

155

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157

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159

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FINAL REMARKS

The goal of this work was to present a different approach in the design of high-rise buildings in the urban context. Contemporary cities are changing quickly, while this could be seen as a positive feature it could also cause the collapse of the urban system. A constant growing population is the symptom of the attraction power of the new metropolises. At the same time, the new mass of people moving towards the cities requires a denser urban tissue. High-rise building have proven to be suitable to solve those issues but as it happened before they have been seen as a tool for increasing profits at the expenses of poor architectural and aesthetic quality. Thanks to the implementation of Genetic Algorithms, this work tried to interlace the connection between architectural quality and aesthetics while at the same time addressing the contemporary city issues. The use of GA provided the opportunity to explore a diverse range of optimal solutions but without cutting out the designers choices. Moreover, a FEM and a Radiation analysis led to a better integration of the environmental boundary condition with the spatial necessity of this architectural typology. The aesthetical result is the representation of the relationship between morphological rules and tectonic process. In conclusion, this work wants to represent an algorithmically empowered solution for the contemporary city issues, integrating fitness and architectural quality with the research of aesthetical values.

161

FINAL REMARKS

162


ACKNOWLEDGMENT Ringraziamenti

Un particolare ringraziamento va al Prof. Alessio Erioli per gli insegnamenti di questi mesi e i consigli per la realizzazione del lavoro. I would like to thank Paul Wintour for his support and precious advices especially in the first steps of this work and for his great willingness in following my work. Un particolare ringraziamento va a mio fratello che mi ha sostenuto e guidato durante lâ&#x20AC;&#x2122;intero percorso universitario insieme ai miei fantastici genitori a cui questo percorso deve molto. Ringrazio Rosa per esserci stata durante tutto questo anno, per avermi sopportato e per essermi stata affianco. Ringrazio la Prof., Nino e Novella per il loro continuo affetto e costante presenza. Allo stesso modo ringrazio Antonio e Alberta per la disponibilitĂ mostrata durante questi anni. Vorrei anche ringraziare Gio, per i pranzi sfiziosi e suoi preziossimi consigli. Grazie a Mattia, Lorenzo, Dmitrij e Ge compagni di questi anni. Un pensiero particolare va a Fiore, Daniele e Lucaaaa amici di sempre nonostante le distanze. Ringrazio anche Eli, Fas, Palla, Giuliett, Elsa, Steve, Elena, Silvia e tutti quelli che hanno condiviso con me i momenti di questo percorso universitario.

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BIBLIOGRAPHY

- John Frazer, 1995, “An Evolutionary Architecture”. Architectural Association Pubblication, London. - Leonardo Benevolo, 2011, “Storia dell’architettura moderna”, Editori Laterza, Bari.

- Mario Carpo, 2011, “The Alphabet and the Algorithm” The MIT press, Cambridge, pp. 51-53. - Mario Carpo, 2016, “Parametric Notations”, in “Architectural Design”, special issue Parametricism 2.0: Rethinking Architecture’s Agenda for the 21st Century, London, John & Sons, 86 (2), pp. 24-29.

- Reiner de Graaf, 2015, “The same architecture that once embodied social mobility

- Branko Kolarevic, 2003, “Architecture in the Digital Age: Design and Manufacturing”, Spoon Press, London and New York, pp. 13-14.

- Alejandro Aravena, 2016, Two billion more people will live in cities by 2035. This could be good – or very bad, “theguardian.com”

- Patrik Shumacher, 2008, “Parametricism as Style – Parametricist Manifesto”,

now helps to prevent it”, Dezeen,

- P. Wintour, 2015, Fitness of Politics Studio, University of Technology Sydney, Sydney, - Aryan Shahabian. (2015 Sept), “Integration of solar-climatic vision and structural design in architecture of tall buildings”. University of Applied Arts, Vienna. - Kate Ascher, 2011, The Heights: anatomy of a skyscraper, Penguin Press, New York, - Neri Oxman, 2010, “The new structuralism. Design, engineering and architectural technologies” in “Architectural Design”, special issue New Structuralism, London, John & Sons, - Farshid Moussavi and Daniel Lopez-Perez, 2009, Seminar “The function of Systems”, Harvard Graduete School of Design, Cambridge MA.

- Gary William Flake, 1998, “The computationao Beauty of Nature”, MIT press, Cambrindge, Massachusetts. pp. 339 - 342 - David Rutten, 2014, “Navigating multi-dimensionallandscapes in foggy weather as an analogy for generic problem solving”, 16th International Conference on geometry and graphics, Austria, - David Rutten, 2013, “Galapagos: on the logic and limitations of generic solver” in “Architectural Design”, special issue Computation works, London, John & Sons, 82 (2), pp. 132 -135. - Robert Vierlinger, 2013, “Multi-Objective Design Interface”, University of Applied Art Vienna, Vienna, pp. 31-33

- Keith Besserud & Neil Katz & Alessandro Beghini, 2013, “Structural Emergence” in “Architectural Design”, special issue Computational Works, London, John & Sons, - Reyner Banham, 1969, “Architecture of the Well-tempered Environment”, University of Chicago Press, Chicago, - Tom Wiscombe, 2010, “Extreme Integration ” in “Architectural Design”, special issue Exuberance, London, John & Sons, 80 (2), pp. 78-87.

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BIBLIOGRAPHY

BIBLIOGRAPHY

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Optima  

Thesis project by Alessandro Cascone - advisor: Alessio Erioli - co-advisors: Paul Wintour - Thesis project done @ Università di Bologna - 2...

Optima  

Thesis project by Alessandro Cascone - advisor: Alessio Erioli - co-advisors: Paul Wintour - Thesis project done @ Università di Bologna - 2...

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