Studio 1

sound mind pavilion

Adam Chown, Ben Sayers, Dan Cruse

Introduction In this thesis project, we undertake the exploration of mental health. Our ambition is to scrutinize architecture’s involvement in healing and develop an approach to mental illness prevention.

We have identified mental health as an emergent concern within today’s wider context.

For this reason, we will be exploring the possibility of environmental noise eradication and mental illness prevention on a larger scale.

In this project, we are designing a pavilion for experimentation purposes. We aim to use the pavilion as a test bed in order to use what we’ve learned to inform future design.

We will be using evolutionary inspired design methods and tools to develop a large number of designs which can be measured and optimised, which is not possible without the use of generative design.

Thesis statement THE PROBLEM

METHODOLOGY

SOLUTION

Mental illnesses have a direct correlation with environmental noise. Medicinal cures are used heavily as a quick fix.

We aim to use the pavilion for experimentation in order to tackle the problem at a larger scale.

Our solution is to manipulate the application of acoustics to present an opportunity for non-medicinal healing through sound therapy.

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We discuss a key issue in a much larger landscape than the site. A narrower topic for discussion is identified as a major cause and a variety of solutions are explored.

02

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DESIGN CONTEXT

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The portfolio concentrates on unravelling existing beliefs and stigmas in relation to our society’s heavy use of medicine to cure mental illnesses. We aim to explore ways in which architecture can prevent reliance on harmful medicinal abuse and aid the prevention of mental illnesses.

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Thesis Context

04 DESIGN APPROACH

THE PROBLEM

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The design exploration and methodology for key decisions

05 APPLICATION

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The identification of improper uses of design and proper uses of design, and how computational methods can leverage the development of an architectural project.

03 METHOD The exploration into the implementation of sound healing as architectural form.

The process of using evolutionary methods to produce different elements of our design, which are measured and analysed with performance metrics

06 DESIGN PROPOSAL The further development of the design.

1.0

the problem

6

1.1

the contemporary pavilion Vanishing Point - Sylvia Lavin on the Contemporary Pavilion

“In the early twentieth century, the pavilion was firmly established as a place of architectural experimentation.” The experimentation within this typology allowed architects to use the pavilion as a test bed for future projects.

Architect

Architect

Sylvia Lavin claims “it has lost its connection to the most pressing issues of the day.” She describes the process of designing pavilions as a manufacture or assembly line with an increasingly emergent number of pavilions being erected each year by a number of different institutions, art programs, biennals

Pavilion

and expos.

Young architects see the pavilion as a stepping stone onto employment rather than for experimental purposes.

“The pavilions of this era shaped future architecture more than permanent buildings, because their very temporariness freed

Project

Project

The Previous Pavilion

Project

Project

Pavilion

The Contemporary Pavilion

Project

them from prevailing habits, enabling them to materialise concepts not yet readily available.”

7

1.2

the future pavilion

The Pavilion as a Test Bed Sylvia Lavin identifies two key aspects in which the pavilion can be successful: by taking inspiration from previous pavilions as an experimentation for future projects, and for the close collaboration between the artist and architect. “generate a complex interaction between art and architecture

Architect

Artist

that produces objects, of which the pavilion might be one, that seek to be situated within complex and extensive networks.”

We are designing a pavilion for experimentation purposes, and aim to use what we’ve learned to inform our thesis project.

Pavilion

Project

The Future Pavilion

Project

The degrading human psyche In this section we explore the emergent issue of mental health. This source of extreme pain for many is often a symptom of environmental noise, the World Health Organisation suggest. We aim to explore the ways in which architecture can aid the prevention of the large scake mental health crisis.

9

1.3

mental health

The Scale of the Mental Health Crisis 1 in 4 people experience mental illnesses each year[1]. Mental illness is the single largest burden of disease in the UK[2], being more common, longer lasting and impactful than other health conditions[3].

There are roughly 6000 suicides each year and it’s the biggest killer of men under the age of 49, with the majority of mental conditions developing within people’s adolescent years[4].

We have identified mental health as an emergent concern within today’s wider context. For this reason we aim to present a thesis project which can aid the development of mental illness prevention.

[1], [2], [3] - https://mhfaengland.org/mhfa-centre/research-and-evaluation/mental-healthstatistics/ [4] - https://www.bbc.co.uk/news/health-41125009 https://www.mentalhealth.org.uk/statistics

4.4 in 100

Post traumatic stress disorder (PTSD)

5.9 in 100

Generalised anxiety disorder

7.8 in 100

Mixed anxiety and depression

3.3 in 100

Depression

20.6 in 100

Suicidal thoughts

2.4 in 100

Phobias

6.7 in 100

Suicide attempts

1.3 in 100

OCD

7.3 in 100

Self-harm

0.6 in 100

Panic disorder

10

1.4

environmental noise How Does Environmental Noise Affect Mental Health?

Mortality

“The evidence from epidemiological studies on the association between exposure to road traffic and aircraft noise and hypertension in ischaemic heart disease has increased

disease

Sleep Disturbance, Cardiovascular

during recent years.”

Environmental noise can have negative effects due to their “micro stressor” characteristic. Similar to other small

risk factors

Blood Pressure, Cholesterol, Blood Clotting, Glucose

disturbances, the build up of these stresses can damage mental health.

“At least one million healthy life years are lost every year due to traffic related noises

stress indicators

Autonomous Response, Stress Hormones

in the western past of Europe.”

Environmental Noise and mental illnesses aren’t typically associated, despite it’s implications. This is why we aim to tackle

feelings of discomfort Annoyance, Disturbance

this problem in the wider context of mental illness prevention.

World Health Organisation - https://www.who.int/quantifying_ehimpacts/publications/e94888. pdf?ua=1

Number of People Affected

11

1.5

medicinal treatment The negative effects of prescribing medicine for the prevention of mental illness

“It is easier for patients to pop a pill than

Prescription

The start of the downward spiral begins with the diagnosis and prescription of medicine, without the doctor knowing the full extents of the illness.

Reliance

make lifestyle changes[1].”

The patient can then begin to become reliant on the medicine as a “temporary fix” to relieve mental stress.

Patients and doctors both take the path of least resistence (prescribing and taking medicines) without finding the cause of the mental illness.

Addiction

The patient begins to rely on the medicine to feel better to the extent they cannot be without it, as they associate the drug with

“30-40% of patients with depression have

feeling good and not taking medicine to

only a partial response to pharmacological

feeling anxious.

and psychotherapeutic interventions[2].”

With the negative impacts of medicinal use in mind, we aim to look at non-medicinal ways mental illnesses can be prevented.

[1] - Qureshi, N., Al-Bedah. A., “Mood disorders and complementary and alternative medicine: a literature review,” Neuropsychiatic Dis Treat., Volume 9, 2013: Pages 639-658.

[2] Dr. Ravi Hira, Cardiology Researcher - Baylor College of Modeicine, Houson, Texas.

Health Implications

Overuse of certain medicines can lead to further health implications, which initially started with an unlinked health issue..

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architectural characteristic.

Sound therapy stands out independently

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Our aim is to create a sound therapy pavilion to demonstrate how the mis-use of medicines to treat mental illnesses can

Mind - https://www.mind.org.uk/information-support/types-of-mental-health-problems/mental-health-problems-introduction/treatment-options/#.XiWD3lP7Sb8

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can be eliminated.

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how environmental noise in public spaces

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be eradicated, while also demonstrating

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scientifically proven benefits.

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with architectural design to achieve

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illnesses which can be largely improved

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as a non-medicinal solution to mental

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which are majorly affected by a change in

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talking therapy and meditation - none of

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depression reveal a variety of activity

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medicinal treatments for anxiety and

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An exploration of the most common non-

based therapies, such as art therapy,

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Identifying the solution to over-usage of medicines.

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the double edged pavilion We’ve identified sound therapy as a method in which architecture can contribute to the prevention of mental illnesses. While it has the potential to benefit the symptom, it can also eradicate the cause - environmental noise.

2.0

DESIGN CONTEXT

Designing like nature To begin contextualising the thesis, we explore the issues with contemporary design methods and demonstrate how we plan to use generative design to mimic evolutionary development.

16

2.1

“How can I tell that I think till I see what I say?”

Research into past theory and what has previously been done aesthetic studies

research for design Aiming to research for future design

Historical, Economic, Political, Social Research

“Where artists, craftspeople and designers are concerned, the word ‘research’ - the r word - sometimes seems to describe an activity which is a long way away from their respective practices.”

The first being research into art and design. historical research, looking into economic, political and social theories are integral to this type of research. research through art and design is a more practical approach and is more interested in materials research and development work through trial and error. Finally, research for art and design produces the end product as an artefact, and is communicated through “iconic/ imagistic communication.”

“How can I tell that I think till I see what I make and do?”

Research through present tense material research and development Sir Christopher Frayling - Research in Art and Design

Research THROUGH Design

Material research and development

“How can I tell what I am till I see what I make and do?”

Research for future design.

To display the results of research in order to communicate its value.

Product as an artefact http://researchonline.rca.ac.uk/384/3/frayling_research_in_art_and_design_1993.pdf Research for Art & Design - Christopher Frayling

Research INTO Design

Research FOR Design

17

2.2

the problem with learning

Output

How do Systems Function?

The general ruleset for systems follows the outline of: input followed by the process

Input

followed by output. More complex versions of this system are appropriate for specific

Optimal Solution

Process

uses. In the case of an architectural project, genetic algorithms can be more useful than artificial neural networks because of the feedback loops which allows for more optimal solutions.

Output Heuristics

Input

Process

Generic System

https://medium.com/

Output

Genetic Algorithms

18

2.3

learning from nature How can Design Replicate Natural Selection?

In this scenario of two birds breeding, each parent has slightly different characteristics (phenomes/genes). The first parent (top) has genes which typically result in a larger physiology. The second parent (bottom) has genes which dictate a small beak. The offspring takes characteristics from both

The process of evolution is constant.

parents - the diagram shows the transfer

The characteristics (phenomes) we see

of the small beak gene from parent to

in organisms today are the most recent

offspring.

iteration in a multi billion year process.

Similarly to nature’s evolution process, we can take elements from two designs (parents) and morph them into one. In this example, we explore various spatial configurations for a pavilion. The first parent (top) displays a smaller grid system which allows for more spaces to fit within our site. The second parent has a greater distance between each space. The result of this crossover is found on the right - a more dense grid and a greater distance between spaces.

https://medium.com/

“Form is the primary instrument through

Form

Inorganic Speciation (Supermanoueuvre) by Iain Maxwell and Dave Pigram

Theory

evolutionary design

Foundation

2.4

Algorithm

19

Generative Design Process

which architecture engages with the world.” Maxwell proposes the migration from “object-centric form production” to the “speciation of mutable architectural models.”

Traditional Architectural Process

Speciation supports the use of analytical

Typology

Supermanoueuvre Architectural Process Form

Typology

Typology

tools which address the deficiencies of typologies and presents a more robust way of qualifying alternative solutions.

Supermanoueuvre

proposes

the

implementation of algorithmic process to

x1

x1

include feedback loops in order to replicate natural selection.

typology can be created without general assumptions. It is this process that we aim to replicate in our thesis.

https://medium.com/

Algorithmic Process

more sophisticated, non-biased form of the

Typology drivers

Through this method of speciation, a much

Materiality Structure Programme

Optimisation Problem

2.5

Start

generative design

Generate Initial Population

4

How do Machines Replicate Natural Evolution?

Calculate Individual Fitness

The generative design process consists of four key stages: design, performance metrics, exploration and data investigation.

left explains these sections in more detail.

In our process, we target an problem we’d like to solve, and create a parametric

Min

design which can adapt evolutionary -

functions

and

Constraint Functions

No

summarised as the design stage.

Random Selection of ParentsHEIGHT

constraint

functions determine the makeup of the

<4

design space, therefore the performance

Crossover to Produce Children

metrics of the design.

is a human element and cannot be carried

Evolutionary Process

? ?

?

? ? ? ?

?

?

?

P P P

Direct Analysis

parametric prioritiies.

https://medium.com/

Stage

3 4

Data Investigation

End

demands the weighing up of a number of

in the direct analysis section

Stage

Exploration

P

out using computational functions, as it

number of different methods, demonstrated

2

Performance Metrics

Mutation of Children

The exploration of the iterations produced

The data investigation can come through a

1

Design

The design of a geometric model which can create many design variations.

The design of a series of performance metrics which can be used to measure the performance of a single design.

End

Objective Functions

Does Yes Generation Satisfy Stop Criteria? Deformation

outline of the process. The diagram on the

Stage

Stage

Max

The diagram on the right describes this

Objective

Start

WEIGHT

MATERIAL

Input Parameter

20

P

The exploration of the modelâ&#x20AC;&#x2122;s design space through a MOGA (multi-objective genetic algorithm).

The investigation of the resulting design data through statistical analysis.

Generation

21

2.6

An initial population is randomly generated with a sparse variety of genomes/phenomes. A small segment of these solutions will be suitable to carry forward.

genetic algorithms

How can we automate generative design with computation?

To carry out an evolutionary process with the use of computation, each function of the generative process must be written in code. The total process of this can be described as â&#x20AC;&#x153;Genetic Algorithms.â&#x20AC;?

Start Generation Generation

Crossover Parent 1 P1 C

P2

Parent 1

Phenomes are selected from two individual solutions (parents) and crossed over to create the next generation (child) which takes phenotypes from both parents.

Crossover Selection Using a set of performance metrics we can decide which phenotype are most desirable in the specific environment.

These individuals are classed as suitable and selected for further crossovers.

This is not a fully automated process and requires human input to decide which design iterations are suitable.

Selection

Mutation Finally, a minority of children will mutate, which develops a larger gene pool, in which new phenomes developed can be carried into later generations.

Mutation Mutation

Does Generation Satisfy Stop Criteria?

Yes

End

No

Random Selection of Parents Crossover to Produce Children

Selection

https://medium.com/

Calculate Individual Fitness

Child

Crossover

In our project, an initial group of designs will be generated and their fitness values are calculated. Depending on their fitness value, a feedback loop of random selection, breeding and mutation is implemented until a suitable design iteration is found.

Generate Initial Population

Mutation of Children

Evolutionary Process

22

2.7

creating the design space

Bias

Continuity

Exploration

Does not fully describe the design problem

Oversimplifies the design

An unknown system requires exploration to determine the feasibility of a solution

Variables in creating the design space

The Design Space A combination of the previously described characteristics.

The design space aims to replicate natural selection gene pools. For example, within the environment in which a species can adapt, what are the possible evolutionary

Unknown System

stages? The species can only crossover and mutate to a certain extent within a given timeframe. To help us develop the design efficiently, we must create a design space which is easily managed.

Bias vs Variance A model with too much bias won’t explore all the possibilities within the system. This could potentially ignore more ‘optimal’ solutions which lay outside the bias. Too much variance, on the other hand, creates too many iterations, of which, very few are viable solutions.

Continuity vs Complexity A completely continuous space negates the

Known System

need for the generative process, as it is too simple and can be modelled parametrically.

It’s important to maintain lower complexity in order to navigate the design space with some logic. https://medium.com/

Variance

Complexity

Exploitation

High Variance creates model that is too flexile to create a solution

Too random for the model to create any feasible design solutions

Exploitation stops the simulation too soon and doesn’t allow for an optimal solution to be found

23

2.8

exploring the design space The Human Interaction with Generative Design

With the creation of the design space in mind, we created a design space which holds a balance of bias and variance, continuity and complexity, exploration and exploitation.

In this exploration, we decide on our spatial configuration in which the spheres represent our desired spaces. With a balanced design space, we were able to generate around 100 iterations which gave enough variety.

Using certain tools we can filter through a large number of iterations by defining what the desired outcome should be.

By selected more suitable solutions, we can carry a strong ‘gene pool’ to the next stage of the process.

https://medium.com/

Generate

Evaluate

Evolve

We must designate a ‘design space’ as a closed system which can generate all possible solutions for the design

This develops measures in which the system judges each design performance.

The use of evolutionary algorithms to search through the design space and select unique high performing designs.

Finite Element Analysis Method

24

2.9

performance metrics How can we Measure our Design?

Finite element analysis sums up the performance of a design element using small discrete elements. This is typically used in structural analysis but is also a useful way of measuring sound on a 2D plane.

Static Methods

Ray Based and Graph Based Methods Ray based analysis measures rays projected from an emmitting source. The way the rays react with itâ&#x20AC;&#x2122;s environment can be measured.

In our method, rays would represent sound waves, and their reflection dynamics can be analysed.

Creating the design space only delivers half the requirements of the optimisation algorithm. The algorithm needs to know what a successful iteration is and what an unsuccessful iteration is.

Performance metrics can be categorised

Physics Based Solvers and Computational Fluid Dynamics

through static and simulation methods.

Generative

design

searches

through

hundreds of iterations in a relatively

This level of design assessment is far too complex for generative design. It calculates the performance of an object with all the forces within the environment acting upon it, to put it in a state of equilibirum.

short period of time, which requires static methods of analysis.

For sound testing analysis, finite element analysis is a good method for measuring sound distribution on a plane (plan or section) at the beginning and end of a generative process. However, ray based methods allow for more precise manipulation as well as a better understanding of how the sound behaves in 3D space.

https://medium.com/

Simulating Performance

Crowd Simulation This method of analysis is useful when studying social interactions within a defined space, but is also too complex for generative design. Simulations can be designed so agents assume human characteristics in order to predict how people navigate within a space for testing before building physical environments

25

2.10

design optimisation

Parameters = X, Y and Z axis

How do we find high performing solutions?

Objectives = Minimise the volume of the box around the box inside

Once we have a design space which

Vector of Input Data

Objective Functions

Constraint Functions

Input parameters that encompass every possible output

The objectives/goals of the optimisation

Constraints describe the feasibility of the possible solutions

has correct input variability and output measurement, we need an optimisation process which replicated that in the natural evolution, in order to find a variety of high performing designs.

The optmisation process is defined by these

z

three stages: 1) input data which describes each iteration within the design space.

2) objective functions which outline the desired outcome of the model. 3) constraint functions which assesses the viability of each design using performance metrics.

Som ‘optimisation problems’ can be solved directly using genetic algorthms, however most of the time, a design will have to be optimised incrementally.

For our design, we’ll need to optimise in three steps, as we describe in more detail later, because our design will have three different optimisation problems.

https://medium.com/

x

y

Constraints = Maintain the volume of the internal box

26

2.11

workshops Softwares and Skills

Python

Octopus

Rhinocerous

Grasshopper

We were introduced to Python through a series of intensive workshops, in which we learnt the basics of the language and some good practices when coding. We were able to test our knowledge by using some basic code to optimise acoustic panels within our design.

Octopus is a multi objective optimisation plugin for Grasshopper, we were shown how to navigate through the basic operations and how to understand the results.

We learned basic Rhino modelling methods when modelling the Jellyfish house, and were guided through with a number of workshops. This has helped us in times where we’ve needed to extrapolate grasshopper models for manual adaptation.

Grasshopper tutorials were very beneficial as we have used a combinantion of grasshopper, rhino and python to generate our forms, grasshopper gave us the ability to parametrically control our ouput and optimise our design using it’s plugins.

Galapogus

TT Toolbox

Biomorpher

Code Academy

Galapagos is similar to octopus, however, Galapagos can only set a singular goal function.

TT toolbox gave us the ability to sort through 1000’s of different iterations with the ability to manually select what output best suited the objectives. Using constraint sliders TT toolbox gives you the ability to sort through iterations dependant of goals set.

Similar to TT toolbox, Biomorpher allows the ability to select through iterations dependant on objectves set. However, Biomorpher is a manual process and gives you more ability to select iteratios based off of what you determine to be the best output.

Code Academy is a great online resource which has catalysed the process of learning to code outside of our python workshops.

Throughout the duration of our thesis project, we have been participating in weekly workshops which equip us with the key tools for generative design methods.

These workshops have given us a wider understanding

of

the

process

and

emphasised the need for evolutionary design in the built environment in a contemporary urban setting.

The human input Despite the process of generative design being theoretically possible without constant user interruption, itâ&#x20AC;&#x2122;s important the human element is retained to ensure the process is properly managed and the performance metrics are weighed up when selecting an iteration to carry through in the development processs.

3.0

method

Developing an auditory experience In this section, we explore the science of sound and how it can be manipulated to create optimal auditory spaces.

Vibrating Medium Particles

30

3.1

introduction

Receiver (Ear)

Emitter (Speaker)

How Does Sound Behave?

Wavelength (Frequency)(Hz)

Before we can start optimising spaces for

Amplitude

sound, we need to understand the principles of sound.

Wavelength (Frequency)(Hz)

Each space is required to react to the sound differently due to the varying properties of

Wavelength (Frequency)(Hz)

increasing frequencies.

Ultimately, sound transmission requires three stages: an emitter, a medium (the

Time/Distance

material which it travels through) and a receiver. In the scenario shown on the right ,the emitter is the speaker, the medium is air and the receiver is the ear. Each element has itâ&#x20AC;&#x2122;s own set of parameters which must be organised correctly before we begin the optimisation process.

Reflection

Absorption

Diffusion

31

3.2

sound therapy types

Anechoic Chamber

How do Traditional Therapy Techniques Differ?

Vibroacoustic

In order to understand how our pavilion

Singing Bowl Therapy

Anxiety

can aid mental illness prevention through sound therapy, we need to assess current

Tuning Fork Therapy

Depression

Brainwave Entrainment

Post Traumatic Stress Disorder

methods of sound therapy.

While it is traditionally used for sound testing purposes, the anechoic chamber is known to provide health benefits because of it’s complete silence.

A number of sound therapy types fit into the category of ‘low frequency therapy’

Dementia

Tuning Forks

Autism & Learning Difficulties

Binaural Sounds

Behavioural & Psychiatric Disorders

Neurologic Music Therapy

Cancer

in which the main benefits come through the medium of vibration transmission. The majority of these methods provide benefits

Holosonic Therapy (Multidimensional Music)

through the stimulation of the patient’s physiology.

Psychogeometric Therapy

High frequency therapies are also common and typically affect the patient’s neurology.

By categorising sound therapies into a number of different sound types, we can start to generate a number of spaces which will be in our design.

typology

frequency

treatable symptoms

32

3.3

sound therapy analysis

0.1-4hz delta

What Benefits do They Provide?

- zero frequency, low frequency and high frequency therapies.

. Focussed Attention . High Level Cognition . Analytical . Stimulant

14-30hz beta

Binaural sound therapy traditionally varies

the difference between the two becomes

. Relaxed Focus . Stress Reduction . Positivity . Accelerated Learning . “Flow” State

8-14hz alpha

are typical of their respective categories

marginally varying frequencies, in which

. REM Sleep . Relaxation . Meditation . Creativity

4-8Hz theta

The sound therapies selected in the diagram

in low frequency. It works by transmitting

. Deep Sleep . Pain Relief . Anti-Aging . Healing . Uncoscious Mind

. Relieve Anxiety . Relieve Depression . Relieve PTSD . Prevent Dementia . Relie

30-100hz gamma

. High-level Information Processing . Cognitive Enhancement . Memory Recall . Peak Awareness

0.1-20hz infrasound 20-20khz sound

. Brain Activation . Evoke Positive Behavioural Response . Brainwave Stimulus

20khz ultrasound

congruent with the patient’s inert brainwave frequency.

124Hz - 114Hz = 10Hz 114Hz

Holosonic therapy works by transmitting

124Hz

a number of higher frwquency sounds together, which affect cell vibration and activation.

With the exception of the anechoic chamber, these therapies all range in frequencies to provide a vast array of benefits and positively affect the long term mental wellbeing of patients.

We aim to replicate these spaces within our pavilion.

anechoic chambers

BINAURAl sounds

holosonic therapy

33

3.4

geometry case study Acoustic Pressure Manipulation

Creating an Optimal Acoustic Pressure

Herzog & De Meuron - Elbphilharmonie, Hamburg

The illustration above shows how we aim to

The Elbphilharmonie Theatre has been

implement Herzog & De Meuronâ&#x20AC;&#x2122;s acoustic

designed

pressure optimisation into our design. By

The geometry of the room is designed

morphing the desired spaces into optimal

specifically for an optimal acoustic

acoustic pressure spaces, we can create a

pressure environment.

Now we have an understanding of what functionality each space will have, we can start to create suitable acoustic environments for the specific frequencies of sound.

Here, we study how the Elbphilharmonie has been designed to enhance the acoustic pressure within the theatre space.

design suitable for each defined space.

to

perform

for

acoustics.

The spaces are specifically designed to carry out a certain function. The first stage

34

3.5

is to decide on the parameters of each space based upon their optimal acoustic conditions. The circulation spaces between can then be outlined in the same way.

sound manipulation Acoustic Pressure

We aim to replicate the successful acoustic environment of the elbphilharmonie by optimising the acoustic pressure for each space. We plan to do this by using ray-based optimisation methods to generatively search through a number of iterations.

These iterations will come from different ways of merging together the spaces outlined in the top diagram, and also from the manipulation of these spaces.

Once

the

spaces

have

been

set

parametrically, we can merge them together to create a more suitable geometry for a more suitable acoustic pressure.

From this stage we can begin to start applying panels which can redirect sound to a particular part of the room or diffuse/ absorb the sound

35

3.6

Material case study Reflecting and Diffusing Sound

Once a space has been optimally designed around acoustic pressures, the materiality must be considered. This adds an extra level of refinement to how the soundwaves react when interacting with the panels.

The materiality of these panels can dictate the likelihood of reflection, diffusion and absorption.

The geometry of these panels also have an effect on the soundâ&#x20AC;&#x2122;s behaviour upon interaction.

Here, we look at the Voxman School of Music

by LMN Architects to discover possibilities within the materiality of optimal acoustic spaces.

Differentiating Panel System

LMN Architects - Voxman School of Music, Seattle

The illustration above shows how we aim to

The Voxman School of Music has a ceiling

implement LMN Architectâ&#x20AC;&#x2122;s panel concept

which is perforated in segments where

into our design. By having a smallnumber of

sound should not be reflected. This has

panel types,, we can dictate the properties

been designed using a generative process,

of individual spaces

and is optimised to direct sound toward the audience.

This diagram demonstrates the next step once the initial spaces are formed based

36

3.7

sound manipulation

off the acoustic pressure optimisation.

The panels types are optimised through the generative design process based on what function is required.

Reflecting and Diffusing Sound

Reflecting upon methods used within the Voxman School of Music, weâ&#x20AC;&#x2122;ve identified two panel differentiators - those where the objective is to reflect sound and those where the objective is to absorb/diffuse sound.

Using a slightly different method however,, without the opening and closing of panels and instead creating smooth and rough panels which replicate the function.

A closer view of the acoustic panels in sectional view shows that the panels would act similar to the acoustic properties within the Elbphilharmonie, which use scalelike perforations to diffuse sound more effectively.

3.8

panel absorption Acoustic Simulation and Conditioning in Vaulted Structures - Adam Hannouch

0.7

F - Diagrid Angled Faces

0.4 Pattern F (0.35)

0.3

Pattern B (0.20)

0.0

Pattern A (0.19) Pattern D (0.14)

0

this research is to analyse various panel

5

10

15

20

25

Frequency (Hz)(10^3)

tessellations in order to diffuse and absorb or

E - Diagrid Pyramid

Pattern E (0.38)

spaces in this paper. The key objective in

noise

D - Parallel Faceted Corner Pyramid

Pattern C (0.62)

0.5

0.1

environmental

C - Staggered Inverted Pyramid

0.6

stereotomic strategies for multi-listener

explores

B - Staggered Pyramid

0.8

faceted

Hannouch

A - Parallel Pyramid

0.9

0.2

Adam

Scattering Performance (Medium Density)

1.0

Scattering Coefficient

37

reverberation

sound with maximum effectiveness.

The graphs compare the results of medium and low density patterns (how many

Scattering Performance (Low Density)

pertrusions per sq metre) which varies

Pattern E (1.00) Pattern C (0.99) Pattern F (0.98)

the results as well as the tessellation

1.0

iterations.

0.9

Pattern B (0.95)

0.8

Pattern D (0.85)

rises above 10kHz, Pattern C and E provide the greatest coefficient in the medium density and low density test respectively, while providing the second greatest coefficient in the other test.

This means for low density tessellations, pattern D is a superior acoustic absorber, and pattern C provides greater absorption for medium density tessellations.

Scattering Coefficient

The results show that when the frequency

Pattern A (0.77)

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

5

10

15

Frequency (Hz)(10^3)

20

25

38

3.9

reflecting the environment Mirrored Facade

The mirror cube is described as the last outpost or the first base station between contemporary cultured society and an untouched natural environment. The concept behind mirrorcube is the relationship between man and nature, and is an observation on how we approach nature both as something enticing but also challenging.

“The mere exposure effect is even stronger when words or images are repeatedly presented subliminally. Simply being exposed to something for a while fosters greater liking for whatever has been presented, but when researchers deliberate expose people to a stimulus on a subconscious level, the exposure still fosters greater liking. That is, even if you have no awareness that you’ve seen something a bunch of times before.” Andy Luttrell, Author of The Mere Exposure Effect.

By subliminally presenting the natural surroundings of the serpentine we can increase a persons familiarity of the site and their surroundings, a sense of familiarity has been likened to creating a sense of contentment and mental well-being.

“The mirror reflects the world around us, showing us the image of a world that look at once familiar, and at once, different.” - Kapoor, 1978.

composing auditory spotlights Through the techniques weâ&#x20AC;&#x2122;ve just explored, our aim is to use genetic algorithms to target specific regions within each space with their respective sound frequencies.

4.0

DESIGN APPROACH

instigating evolution The first stage in the generative design process is to design the building. This section sets out the rulesets and parameters for the generative development to follow.

42

4.1

mapping sound

How Does This Affect Our Design?

Recreating the site using an online tool (noisetools.net) allows us to visualise levels of environmental noise around the site using rules of thumb for noise emittance and reflectance.

The data extracted would indicate that road noise is partially blocked by the line of trees sitting on the boundary.

Toward the centre of the site there lies a quieter region. This may be an appropriate placement for lower frequency spaces which require less interference from environmental noise.

Environmental Noise (dB)

72DB 53DB 45DB 30DB 20DB

43

4.3

SOUND DISTRIBUTION

N

How Will Sound be Distributed Throughout the Respective Spaces?

Each space is designed to receive specific qualities and quantities of sound. In the larger zone at the entrance of the pavilion, visitors would hear a low frequency sound

Least

covering the whole space. As visitors move through the pavilion, the frequency rises as the sound focal point shrinks to create a sharp high pitch sound at the end of the journey.

Most

44

4.4

ACOUSTIC EMITTERS

Where Do They Need To Be Positioned And How Should They Act? Within the model, the acoustic emitters

Tight focus sound emission, similar to that of holosonic sound beams.

may vary in angle of transmission, positioning within the space and the angle of transmission. These can all influence the geometry of individual spaces.

A wide spread emission of sound that would cause a lot of unwanted noise pollution within the pavillion.

Change to position of the emitter but retain the focus of the sounds direction

Tight focus sound emission, similar to that of holosonic sound beams.

A wide spread emission of sound that would cause a lot of unwanted noise pollution within the pavillion.

The spread of sound emission from its source

Change the target point which will change the direction of sound from the same emitter

45

4.5

path distribution SUBTITLE

1

2

3

The route within the pavilion can vary greatly and is restricted to the properties of the room. The route needs to be primarily based off the sound distribution within the respective space.

The final outcome ensures that the path meanders through the sound distribution zones fully, where the path starts wider and finishes narrow, in order to guide visitors through the auditory experience.

the first result from the iteration testing did not provode the result we were looking for, this iteration can not be considered feasilbe as the path or â&#x20AC;&#x2DC;journeyâ&#x20AC;&#x2122; has a number of sharp curves which is not idea when testing for an accoustic envirnoment as it creates narrow pathways, making it harder to manipulate acoustic rays towards a specific point

the second iteration testing headed better results, however, this outcome can not be considered feasiable as the path sticks to the edges

4

Determining the design space Now the basic design functions and parameters are set, we can start to test different iterations within our specified design space.

5.0

APPLICATION

initiating the process Our generative design process consists of 3 stages which are split by user input.

1 - Spatial Configuration 2 - Acoustic Pressure Optimisation 3 - Panel Tessellation These stages will each be tested and developed using a grasshopper plugin called Snail, which is a ray-based method of analysis sound waves in spaces.

Identify maximum site area

49

5.1

Map site decibel levels

Stage 1: Spatial Arrangement

Stage 2: Optimal Volumes

Define the design space

overview Acoustic Pressure Performance

2

3

Choose an acoustic panel system

Apply panels to optimised surface

4

Scale and offset the panel system

NO

Define the area of each room type to be measured for its interraction with the sound source emisssion

Singular, focused sound projection

YES

Divide the design space into a grid

The flow diagrams describes the entire

Stage 3: Refinement Panelisation

Identify four room types

1 Does this sit within the lowest decibel leves across the site?

Optimal Volumetric Solution

Spatial Arrangment Option

Even, wide spread sound distribution

Run the same simulation of sound with the new panel system

Choose a sound source emitter

generative process. The process has been split into 3 logical stages in order to simplify

NO

Choose a direction for the emitted sound

Populate the grid with initial room massing

the development and allow for more user

YES

control.

Are these a maximum size within the spatial grid?

Stage 1 is designed to arrange the spaces on

Manipulate the internal volume to redirect the emitted sound

NO

and how loud it is on site.

Count the intersection points of the simulated sound emission and internal floor surfaces

Increase Room Size

site in relation to proximity to one another,

Simulate the reflection of sound within the room

YES

Stage 2 of the development concerns itself with the creation of the geometries to react optimally to acoustic pressures and properties which we intend to use in these

Choose different grid population

YES

Does this allow for the maximum intersection of sound in the given area?

Do the rooms clash & intersect with one another?

Is this the largest cumulitive sum of intersection points?

NO

YES

spaces.

NO Count the intersection points of the simulated sound emission and internal floor surfaces

Stage 3 is in reference to the panel system and how we can use a number of panel types to optimally reflect or diffuse sound in an enclosed space.

Does this allow for the maximum intersection of sound in the given area?

NO

Is there enough spacing betwen rooms to minimise internal noise pollution YES

NO

Is this the largest cumulitive sum of intersection points?

YES

Spatial Arrangment Option

Optimal Volumetric Solution

Optimal Acoustic Panel System

50

5.2

step 1 descriptive geometry

8 5

Underlying Rule Sets

X Divisions

The spatial configuration follows a number

Y Divisions

of set rules within the design space, which allows enough variation and less bias.

5 Rooms

X Y

The grid allows for a range of subdivisioons while the number of spaces and size of spaces also vary within reason until we find a solution which is optimal.

7

1

2

3

4

Number of Rooms 4

6 Rooms

51

5.3

step 1 inputs and measures What Are The Rules And How Is It Measured?

Input Parameters We use are using a variety of number sliders allow for the manipulation of varying data inputs feeding into our parametric model, modelled using the grasshopper software to allow for complete control over the iterations.

of iterations which we can then assess with the help of performance metrics.

The Genetic Algorithm allows us to explore an exponential amount of design options in order to best optimise the measureable outputs. By doing so we can see options for various design outputs that may achieve similar measures.

Divide Chosen Site Area (X Axis) 5

6

7

4

5

8

6

5

We measure certain outputs of the design in order to be able to filter out unwanted design solutions. In this case we want to maximise the interaction of sound within certain areas of the room.

Min

Spatial Arrangement Iterations

Number of Rooms 4

Output Measures

Interaction with Site Decibel Levels

Divide Chosen Site Area (Y Axis)

Correct inputs and measurements allows us to search the design space for a number

Generative Algorithm

Max

Floor Area per room (sqm) Min

Max

Distance Between Rooms 6

Min

Cycle Through Room Locations

Max

Size of Complete Pavillion (m3) Min

Manually Driven

Algorithmic Iteration

Found through the manual inputs of the parametric data sliders

Found through filtering the measureable outputs to either their maximum or minimum values.

Here we can filter out unwanted design options and see multpile options that offer the same output.

Max

52

5.4

step 1 iterations Finding An Optimal Solution

This is just a small number of iterations which was generates by the genetic algorithm.

Video Click Here

Divide Chosen Site Area (X Axis)

Divide Chosen Site Area (Y Axis) No. Rooms

8

5.5

53

6

6

Room Location 10 8

7 5

4

6

2

step 1 selection

70 60

6 5

5

area per room (sqm)

4

4

Cumulitive Distance Between Rooms 70

Divide Chosen Site Volume of structural Area (X Axis) system (m3) 8

No. Rooms 6

6

7 5

4

6

2

30

0

10

5

area per room (sqm) 70 60

6 5

50 40

Room Location

8

60 50

40

Divide Chosen Site Area (Y Axis)

4

4

Cumulitive Distance Between Rooms

Volume of structural system (m3)

70 60 50

50 40 40

30

0

Separating The Good Designs From The Bad Designs

By manually adjusting the parameters in favour of the more desirable features using an online tool, we can filter through the less desirable designs to find more optimal solutions.

Divide Chosen Site Area (X Axis)

Divide Chosen Site Area (Y Axis) No. Rooms

8

6

6

Room Location 10 8

7

70 60

6 5

5 4

6

2 5

area per room (sqm)

4

4

0

Cumulitive Distance Between Rooms 70

Volume of structural system (m3)

Divide Chosen Site Area (X Axis)

No. Rooms 8

6

6

7

10

5 4

6

2

30 5

area per room (sqm) 70 60

6 5

50 40

Room Location

8

60 50

40

Divide Chosen Site Area (Y Axis)

4

4

0

Cumulitive Distance Between Rooms 70

Divide Chosen Site Volume of structural Area (X Axis) system (m3) 8

No. Rooms 6

6

7

10

5 4

6

2

30 5

area per room (sqm) 70 60

6 5

50 40

Room Location

8

60 50

40

Divide Chosen Site Area (Y Axis)

4

4

0

Cumulitive Distance Between Rooms 70 60 50

50 40 40

30

Volume of structural system (m3)

54

5.6

step 1 outcome

Choosing The Design To Carry Through To The Next Stage The outcome of the spatial configuration generation exercise was based upon the previous steps, in which we chose to favour spaces which were further from one another to avoid interfering sounds as well as their suitability within their respective positioning on site.

55

5.7

step 2 descriptive geometry Underlying Rule Sets

The geomatry variation follows a number

Scale Factor: 1

Scale Factor: 1

of rules, such as the scale and how it is morphed at the top and bottom.

Scale Room

Morph Spherical Volume Scale Factor: 0 Scale Factor: 1 Scale Factor: 2

Scale Factor: 1

Scale Factor: 0 Scale Factor: 1 Scale Factor: 2

Morph Top Semi Sphere Scale Factor: 2

Morph Bottom Semi Sphere

Scale Factor: 1 Scale Factor: 0 Scale Factor: 2

Scale Factor: 1

Scale Factor: 2

56

5.8

step 2 inputs and measures What Are The Rules And How Is It Measured?

Correct inputs and measurements allows us to search the design space for a number

Generative Algorithm

Input Parameters We use are using a variety of number sliders allow for the manipulation of varying data inputs feeding into our parametric model, modelled using the grasshopper software to allow for complete control over the iterations.

of iterations which we can then assess

The Genetic Algorithm allows us to explore an exponential amount of design options in order to best optimise the measureable outputs. By doing so we can see options for various design outputs that may achieve similar measures.

Morph Top Semi Sphere

with the help of performance metrics.

5

6

7

1

8

2

Sound Source Location 4

We measure certain outputs of the design in order to be able to filter out unwanted design solutions. In this case we want to maximise the interaction of sound within certain areas of the room.

Single Point Sound Focus (1 Person) Min

Morph Bottom Semi Sphere 0

Output Measures

5

Sound Emission Direction

Max

Small Area Sound Focus (2 Person) Internal Volume Morphing Iterations

Min

No. Sound Rays

Max

Quarter Room Sound Distribution 6

Min

Max

Half Room Sound Distribution Min

Scale Room

No. Sound Rays

Max

57

5.9

step 2 iterations Finding An Optimal Solution

Click Here

Click Here

58

5.10

Step 2 iterations Finding An Optimal Solution

Click Here

Click Here

Morph Top Semi Sphere 8

59

5.11

Morph Bottom Semi Sphere 6

Sound Source Location 6

Sound Emission Direction 10 8

7

70 60

6 5

5 4

6

2 5

Scale Room

4

4

Sound Focus Direction 70

Morph Top Semi Sphere 8

6

Sound Source Location 6

7 50 40

10

5 4

6

2 5

Scale Room 70 60

6 5

30

0

Sound Emission Direction

8

60

50

40

Morph Bottom Semi Sphere

4

4

Sound Focus Direction 70 60 50

50 40 40

30

0

step 2 selection Separating The Good Designs From The Bad Designs

By manually adjusting the parameters in favour of the more desirable features using an online tool, we can filter through the less desirable designs to find more optimal solutions.

Morph Top Semi Sphere 8

Morph Bottom Semi Sphere 6

Sound Source Location 6

Sound Emission Direction 10 8

7

70 60

6 5

5 4

6

2 5

Scale Room

4

4

0

Sound Focus Direction 70

Morph Top Semi Sphere 8

6

Sound Source Location 6

7 50 40

Sound Emission Direction 10 8

60

5 4

6

2

30 5

Scale Room 70 60

6 5

50

40

Morph Bottom Semi Sphere

4

4

0

Sound Focus Direction 70

Morph Top Semi Sphere 8

6

Sound Source Location 6

7 50 40

Sound Emission Direction 10 8

60

5 4

6

2

30 5

Scale Room 70 60

6 5

50

40

Morph Bottom Semi Sphere

4

4

0

Sound Focus Direction 70 60 50

50 40 40

30

60

5.11

step 2 outcome

Choosing The Design To Carry Through To The Next Stage This design ended up getting carried through to the next stage due to itâ&#x20AC;&#x2122;s suitability to the specific spaces weâ&#x20AC;&#x2122;re designing.

61

5.12

step 3 inputs and measures What Are The Rules And How Is It Measured?

Correct inputs and measurements allows us to search the design space for a number of iterations which we can then assess with the help of performance metrics.

Input Parameters

Generative Algorithm

We use are using a variety of number sliders allow for the manipulation of varying data inputs feeding into our parametric model, modelled using the grasshopper software to allow for complete control over the iterations.

The Genetic Algorithm allows us to explore an exponential amount of design options in order to best optimise the measureable outputs. By doing so we can see options for various design outputs that may achieve similar measures.

Number of Attractor Points 1

2

3

Min

Distribution of Attractor Points

5

No. Sound Rays

Max

Small Area Sound Focus (2 Person) Panelisation Iterations

4

We measure certain outputs of the design in order to be able to filter out unwanted design solutions. In this case we want to maximise the interaction of sound within certain areas of the room.

Single Point Sound Focus (1 Person)

4

Depth of Acoustic Panel

Output Measures

Min

No. Sound Rays

Max

Quarter Room Sound Distribution 6

Scaling of Acoustic Panels

Min

No. Sound Rays

Max

Half Room Sound Distribution Min

No. Sound Rays

Max

62

5.13

step 3 iterations Finding An Optimal Solution

This is just a small number of iterations which was generates by the genetic algorithm.

Click Here

Click Here

63

5.14

step 3 iterations Finding An Optimal Solution

This is just a small number of iterations which was generates by the genetic algorithm.

Click Here

Click Here

64

Number of Attractor Points

5.15

8

Distribution of Attractor Points 6

Depth of Acoustic Panel 6

Scaling of Acoustic Panels 10 8

7 5

4

6

2

step 3 selection

70 60

6 5

5

Sound Focus Direction

4

4

Number of Attractor Points 70

8

6

Depth of Acoustic Panel 6

7 50 40

10

5 4

6

2 5

Sound Focus Direction 70 60

6 5

30

0

Scaling of Acoustic Panels 8

60

50

40

Distribution of Attractor Points

4

4

70 60 50

50 40 40

30

0

Separating The Good Designs From The Bad Designs

By manually adjusting the parameters in favour of the more desirable features using an online tool, we can filter through the less desirable designs to find more optimal solutions.

Number of Attractor Points 8

Distribution of Attractor Points 6

Depth of Acoustic Panel 6

Scaling of Acoustic Panels 10 8

7

70 60

6 5

5 4

6

2 5

Sound Focus Direction

4

4

0

Number of Attractor Points 70

8

6

Depth of Acoustic Panel 6

7 50 40

Scaling of Acoustic Panels 10 8

60

5 4

6

2

30 5

Sound Focus Direction 70 60

6 5

50

40

Distribution of Attractor Points

4

4

0

Number of Attractor Points 70

8

6

Depth of Acoustic Panel 6

7 50 40

Scaling of Acoustic Panels 10 8

60

5 4

6

2

30 5

Sound Focus Direction 70 60

6 5

50

40

Distribution of Attractor Points

4

4

0

70 60 50

50 40 40

30

65

5.16

step 3 outcome

Deciding On A Final Design The final stage of the process was decided based upon how well the rays performed in their respective spaces. The performance metrics for this were based on how we wanted the sound to be distributed throughout the pavilion, as per the sound disribution

development

outlined previously.

which

was

process complete At this stage, we have a design which is more suitable than a manually designed pavilion.

Hundreds of iterations have been explored and assessed in three stages which has given far more variability and complexity than we could expect to extrapolate without the use of the generative design method or tools.

6.0

design proposal

68

6.1

floor plan

69

6.2

roof plan

70

6.3

long section

71

6.4

short section

72

6.5

elevations

North Elevation

South Elevation

73

6.6

elevations

East Elevation

74

6.7

exploded axonometric

1. Mirrored cladding panel 2. MDF fixing panel 3. Aluminium C channel 4. End cone and C channel fixing point

Full Construction System Breakdown.

5. Space frame node 6. Steel space frame end cones 7. Steel space frame tube

1

8. Suspended steel frame fixing rod 9. Suspended steel frame

2 3 4 5 6

10. Steel space frame node 11

11. Mirrored cladding panel

12

12. MDF fixing panel

13 14

17. Steel space frame end cones

16

18. Aluminium flashing

17

19. Space frame pad

8

18

9

19

21 22

23

Details are not to scale.

15. Space frame node

15

20

Please find annotations on 6.9

14. Space frame end cone connection 16. C channel end cones

7

10

13. Aluminium C channel

20. Concrete strip foundation 21. Acoustic panel aluminium frame 22. GFRC acoustic panel 23. Excavation form work

75

6.8

3D section

Please find annotations on 6.9 Details are not to scale.

76

6.9

detail overview

Key Construction Detail Callouts

Section A Section B Section C Section D Section E

Please find annotations on 6.7 Details are not to scale.

77

6.10

wall and floor detailing Cladding System and Foundations

The main purpose of the wall buildup is to primarily support the space frame span, but secondarily to support the cladding system.

Section A - Roof to wall to floor detail Exterior

Section B

Section B - Roof to wall to floor detail Interior Section C - Wall to floor detail

Section A

Please find annotations on 6.7 Details are not to scale.

Section C

78

6.11

roof detailing

Space Frame Structure The main roof build up is built with a space frame - trusses connected laterally. The main objective is to suspend the acoustic panel system from via ceiling cables. It is

Section D

not a particularly heavy structure but must be able to withstand a large span.

Section C - Space frame connection detail Section D - Roof to wall detail Upon reflection, the space frame is too

3

connection

Details are not to scale.

1

5

1. Bolt used for connection 2. Conical part used for connection 3. Wide nut-like piece used for

Please find annotations on 6.7

6

8

the hole opened in the bolt shaft

Section E

1. Bolt used for connection 2. Conical part used for conneciton 3. Wide nut-like piece used for connection 4. Sphere used for connection 5. Gasket used for connection 6. Cylindrical spindle passing through the hole opened in the bolt shaft 7. Channel opened on the facing surfaces of the wide nut-like piece 8. Pipe welded to the conical part

4

7

2

SLEEVE

4. Sphere used for connection 5. Gasket used for connection 6. Cylindrical spindle passing through

NODE

Channel opened on the facing surfaces of the wide nut-like piece

BOLT

DOWEL PIN

7.

by 60-70%.

8. Pipe welded to the conical part

dense for this usage, and would be reduced

END CONE

Click for Video Walkthrough

conclusion Working towards the completion of the pavilion using generative design methods has been a steep learning curve and has been a rewarding process, but has also had itâ&#x20AC;&#x2122;s limitations.

The specialisation of optimising acoustic performance is traditionally kept within the realms of acoustic/sound engineers, and for this reason, the design may be lacking further complexity with material properties. A lack of software knowledge and computing power has led to a time consuming process to find correct solutions.

Despite these limitations, the group worked well as a collective to produce the final output, in which we assumed separate roles. Presentation, Construction and Software were each a key role but not restrictive, so we have each taken a lot of experience from each area.

We each feel as though weâ&#x20AC;&#x2122;ve gained a significant body of knowledge with regards to computational design and softwares while working on our thesis.

Our focus is now on how we can apply this new knowledge and project outcome to our thesis at a larger scale.