IAAC // Digital Matter - CoRAAL 1.0 (Booklet) by Elena Petruzzi

MASTER IN ADVANCED ARCHITECTURE Digital Matter

2020/21 CoRAAL 1.0 Coastal Risk Assessment ALgorithm 1.0

BARCELONA

MASTER IN ADVANCED ARCHITECTURE Project Title: CoRAAL 1.0

Research Studio: Digital Matter

Faculty: Areti Markopoulou, David Andrés León, Raimund Krenmüller Faculty Assistant: Nikol Kirova

Elena Petruzzi Laukik Lad Matevi Genne Aqeel Sourjah

INDEX 01 THE PROBLEM 02 OBJECTIVE 03 THE CASE STUDY 04 RESEARCH QUESTION 05 STATE OF THE ART 06 METHODOLOGY

06 | 1

ANALYSIS 06 | 1.1

CATEGORIES AND TOOLS

06 | 1.2 LIMITATIONS

EVALUATION

06 | 2

06 | 2.1 THE SIMULATION

06 | 2.2 GENERAL STRATEGIES

06 | 3

INTERVENTION

06 | 3.1 METHODOLOGY

06 | 3.2 STRATEGIES

06 | 3.3 PSEUDO CODE

06 | 3.4 REDISTRIBUTION

06 | 3.5

MATERIALS 06 | 3.5.1 REUSABLE BUILDING MATERIALS

06 | 3.5.2 BAMBOO 07 CONCLUSIONS 08 2100 VISION 09 REFERENCES 5

Aknowlegdement We would never have been able to complete this project without great support of friends, family, faculty and staff. We would like to express our deepest gratitude to our advisors, Areti Markopoulou, David Andrés León, Raimund Krenmüller and Nikol Kirova to guide us through this challenging experiment and to provide us with continuous courage and determination.

Abstract According to the UN, around 40% of the world‘s population is threatened by the rise of sea level and other climatic conditions that are associated with it. CoRAAL 1.0 (Coastal Risk Assessment ALgorithm 1.0) is a two-part computational system of monitoring and adapting contexts affected by sea-level rise. The case study of the Funafuti island (capital of the Tuvalu atoll) is used to develop the framework for resource monitoring, evaluation, and adaptation. The atoll faces severe sea-level rise impacts within the next 100 years. Furthermore, there is causation to other environmental issues such as coastal erosion, salinization of soil, and flooding. Primarily, the research looks at the identification and localization of resources that are vital for the survival of the island. This is followed by analyzing the risk level of depletion of the identified resources. The constantly changing environmental conditions make dynamic adaptation a key feature of the project. The use of advanced computational tools such as NDVI, Semantic Segmentation Machine Learning, and Elk help cater to the reiterative nature of the analysis, bringing in a dynamic approach to visualize changes in the environment in real-time. The resources on the island are categorized into six main parts: vegetation/ agriculture, buildings and building materials, energy, connectivity, and water supply. Secondly, the project uses the data compiled during the continuous analysis stage to develop protocols for the island. The aim is to propose informed development strategies based on incremental design scenarios to mitigate the effects of sea-level rise and to maximize the welfare

An algorithm is developed to first discretize the island into clusters, then classify the resources into these clusters, and finally identify how vulnerable each cluster is to sea-level rise. The vulnerability is assessed by taking into account areas that would be affected by sea-level rise according to a projected timeline. Finally, the project aims to provide protocols for improving the welfare of the inhabitants while mitigating the effects of sea-level rise. The proposed protocols are classified into four categories: Adapt, Retreat, Protect and Preserve. According to the analysis of data received from the algorithm as well as the external contextual forces, the developed strategy would specify which are the appropriate protocols that are to be implemented. As part of catering to maximize welfare conditions of the inhabitants in the future amidst the context of sea level rise, the project also looks into exemplary living conditions to use it as a benchmark for the project.

THE PROBLEM Sea level rise is caused primarily by two factors related to global warming: the added water from melting ice sheets and glaciers and the expansion of seawater as it warms. As humans continue to pour greenhouse gases into the atmosphere, oceans have tempered the effect. The world’s seas have absorbed more than 90 percent of the heat from these gases, but it’s taking a toll on our oceans. Experts predict a rise of around 2.4m by the year 2100 which will affect different countries around the world on different scales.

01.1

S L R G LO B A L I M PAC T

According to the UN, around 40% of the world’s population is threatened by the rise of sea level and other climatic conditions that are associated with it. Sea level rise would cause a widespread global refugee crisis around the world while also causing more hurricanes to destroy livelihoods and economies and also cause widespread water, food, and health crises.

coastal edges at risk

GLOBAL REFUGEE CRISIS

Global Mean Sea Level Rise

MORE POWERFUL HURRICANES

Relative Sea Level Rise

relative to present day

Observations

RCP 2.6

DESTROYED ECONOMIES

FOOD AND WATER CRISES

HEALTH CRISIS

Risk to coastal geographies at the end of century

SCENARIOS

RCP 8.5

No-to moderate response Maximum Potential Response Level of additional risk due to sealevel rise -110cm/RCP 8.5 (Upper Likely Range) Very High

-84cm/RCP 8.5 (Median)

High -43cm/RCP 2.6 (Median)

Moderate

Undetectable -0cm(Present Day)

1900

1950

2000

2050

2100

Resource Rich Coastal Cities

Urban Attal Islands

Latrge Tropical Agriculture Deltas

Arctic Communities

OBJECTIVE Develop an adaptive system of protocols to analyze, evaluate and intervene in a context affected by sea-level rise to mitigate its effects using computational tools which translate environmental conditions on remote urban atoll islands into data.

THE CASE STUDY

03.1

LO C AT I O N

Tuvalu is a group of 9 urban atoll islands with a population of around 12000 people located in the pacific ocean. Tuvalu could be one of the first nations to be significantly impacted by rising sea levels due to global climate change. These islands already face the full force of the sea during high tide where constant flooding and soil salinization added to the already limited resources make living on the island difficult. Funafuti island (capital of the Tuvalu atoll) is used to develop the framework for resource monitoring, evaluation, and adaptation.

NANUMEA NIUTAO

NANUMANGA

IF IC

C E

NUI

VAITUPU

NUKUFETAU NIUTAO

UMANGA

IF IC

ATOLL ISLANDS

O FUNAFUTI

C E

NUKULAELAE

A N

TUVALU

NUI

VAITUPU N

NUKUFETAU

IF IC

100 km

NIULAKITA

ATOLL ISLANDS

12,000

PEOPLE

C E

A N

FUNAFUTI NUKULAELAE

VAITUPU

ATOLL ISLANDS NIULAKITA

12,000

PEOPLE

2876

RAINFALL PER ANNUM

FUNAFUTI NUKULAELAE

12,000

2876

RAINFALL PER ANNUM

TUVALU

6.7% Infrastructure

60%

33.3% Forest

Agriculture

FOOD SUPPLY 2.5% Imported

50%

5% Banana 10% Fish

Pulaka

10% Coconut

E T

LAND USE

P A C I F I C

O C E A N

100 km

FUNAFUTI CAPITAL OF TUVALU Coral atoll HIGHEST POINT : 4.6 m ASL POPULATION : 6,320 people

(60% of Tuvalu population)

SEA LEVEL RISE IMPACT

COASTAL EROSION

10 km

SALINIZATION OF SOIL

FLOODING

RESEARCH QUESTION How can computational tools help us translate environmental conditions on remote urban atoll islands into data and make informed decisions to develop adaptive strategies to mitigate effects of the rising sea level in the long term?

STATE OF THE ART Many projects related to the topic of advanced computational analysis were studied to develop this research project. They have been a good source of inspiration and a solid starting point from which new systems have been designed. The two most significant ones are the 4D islands developed by Ben Pollock and Oscar McDonald and Coastal Classifiers by the Harte Research Institute.

05.1

4D ISLAND

Developed by Ben Pollock and Oscar McDonald, 4D island aims to use data to improve the urban and architectural design in the face of the uncertain future of climate change. This project asks the question of how computational tools can help us translate environmental conditions on remote urban atoll islands into data and make informed decisions to develop adaptive strategies to mitigate the effects of the rising sea level in the long term.

SIDS EXPERIENCE THE UNBALANCED EFFECTS OF CLIMATE CHANGE: THEY ARE THE MOST AT THREAT NATIONS FROM CLIMATE CHANGE DESPITE THEIR SMALL EMISSION CONTRIBUTIONS

CO2 em

Global Physical Threats of Climate Change to SIDs

Level of Threat Sea Level Rise

Invasive Species Migration

Marine Ecosystem Degradation

Coral Bleaching Reef Erosion

Mangroves Loss

Cyclone Activity

Loss of Fresh Water Lens

Ocean Acidity

Increase Coastal Erosion

Biological

Temperature

Increase in Storm Surge Damage

Loss of Natural Resources

Geophysical

Poor Coastal Management

Local Anthropogenic Threats to SIDs

Poor Waste Management

Rainfall

Displacement of People

Reduction of Economic Stability

Damage From Extreme Weather

Loss of Settlements

Socioeconomic

Poor Strategic Management

SOURCES : INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE [IPCC] AR5 REPORT MINISTRY OF ENVIRONMENT AND ENERGE MALDIVES UNDP MALDIVES THE WORLD BANK

05.2

C OA S TA L C L A S S I F I E R S

Tracking changes in the coastline and its appearance is an effective means for many scientists to monitor both conservation efforts and the effects of climate change. That’s why the Harte Research Institute at TAMUCC (Texas A&M University-Corpus Christi) decided to use Google Cloud’s AutoML Vision classifiers to identify attributes in large data sets of coastline imagery, in this case, of the coastline along the Gulf of Mexico.

METHODOLOGY

06.1 A N A L Y S I S The project first identifies resources that are important for the continuous habitation of the island. These resources are categorized into 6 main parts; Vegetation, Fresh Water Sources, Connectivity, Energy, Built Area, and Building Materials. After which the following tools are used to analyze these resources to convert the physical environment into data points.

- Rhinoceros 7 - Grasshopper - Semantic Segmentation Machine Learning - NERO- NVDI - Elk - Open Street Maps - Google Earth - Government Publications

06.1.1 C A T E G O R I E S

A N D

T O O L S

The project first identifies resources that are important for the continuous habitation of the island. These resources are categorized into 6 main parts; Vegetation, Fresh Water Sources, Connectivity, Energy, Built Area, and Building Materials. After which the following tools are used to analyze these resources to convert the physical environment into data points.

- Rhinoceros 7 - Grasshopper - Semantic Segmentation Machine Learning - NERO- NVDI - Elk - Open Street Maps - Google Earth - Government Publications

TOOLS

GOALS

CATEGORIES

AGRICU�TURE

LOCATION

TYPOLOGIES

BUILDINGS

LOCATION

TYPOLOGIES

MATERIALS

TYPES

QUANTITY

ENERGY

SOURCE

LOCATION

CONNECTIVITY

CONNECTION TYPOLOGIES

WATER

SOURCE

QUALITY

ELK

NERO

SEMANTIC SEGMENTATION

REUSABILITY

(MACHINE LEARNING)

ANALOG PROCESS DISTRIBUTION

06.1.1.1 V E G E T A T I O N Vegetation is an extremely important resource that helps determine the suitability for life to live in a particular context. The Data on vegetation was collected first through the use of open street map data which was run through the elk plugin in grasshopper to get an output of the land use of the island. After which google earth satellite images were processed through Nero which uses NDVI (Normalized Differential Vegetation Index) to output the quality of the vegetation. Semantic segmentation machine learning algorithms were then trained and used to identify and understand the type of vegetation that was available. All the data collected was then uploaded onto a rich map as superimposed layers.

OSM DATA

ELK

INPUT

TOOL

A B C D E F G H I 1 2 3 4 5 6 7 8 9 10 11 12 13 14

K L

OUTPUT

LAYERS

WOODLAND

SHRUBS TIRO PITS

taro pit pulaka

coconut

pulaka

coconut

shrub

RICH MAP

shrub

TYPOLOGIES

NDVI

taro pit

LAND USE

1 200 2 400 3 A 4

1000

800

F 600 G

1200

1400

8 5

1600

1800

2000

2200

2400

13 10

2600

14 11 0m

200

400

600

800

1000

1200

2800

1400 12

1600

1800

2000

2200

2400

2600

13 WOODLAND TIRO PIT

SHRUBS 0m

200

400

600

800

1000

1200

1400

1600

1800

SATELLITE

NERO

INPUT

TOOL

A B C D E F G H I 1 2 3 4 5 6 7 8 9 10 11 12 13 14

K L

OUTPUT

3D POINTS

taro pit pulaka

coconut

pulaka

coconut

shrub

RICH MAP

shrub

TYPOLOGIES

NDVI

taro pit

LAND USE

LOCATION :

URBAN FABRIC

URBAN FABRIC / AIRPORT

LOCATION :

URBAN FABRIC LOCATION :

URBAN FABRIC / AIRPORT LOCATION :

URBAN FABRIC

URBAN FABRIC / AIRPORT

-1.0 -0.2 -0.1 -1.0 0.0 -0.2 0.1 -0.1 0.2 0.0 0.3 0.1 0.4 0.2 0.5 0.3 0.6 0.4 0.7 0.5 0.8 0.6 0.9 0.7 1.0 0.8 0.9 1.0

Water / Artiﬁcial cover

3,66 %

Bare Watersoil / / Dead vegetation Artiﬁcial cover

3,66 %

Shrub / Grassland Bare soil / Dead vegetation

2,35 %

42,80 %

2,35 %

31,33 % 23,76 %

38,50 % 42,80 %

Abundant / Vigorous Shrub / vegetation Grassland

13,94 %

Abundant / Very abundant / Vigorous vegetation Vigorous vegetation

1,10 %

Very abundant / Vigorous vegetation

1,10 %

13,94 %

23,76 %

37,04 % 31,33 %

38,50 %

5,52 %

37,04 %

1 200 2 400 3 600 4 800 5 1000 6 1200 7 1400 8 1600 9 1800 10 2000 11 2200 12 2400 13 2600 14 2800 0m

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

LOCATION :

COASTLINE �AGOON SIDE

COASTLINE OCEAN SIDE

N N

LOCATION :

LOCATION : COASTLINE �AGOON H6 SIDE

LOCATION : COASTLINE OCEAN K7 SIDE

COASTLINE �AGOON SIDE

COASTLINE OCEAN SIDE

-1.0 -0.2 -0.1-1.0 0.0-0.2 0.1-0.1 0.2 0.0 0.3 0.1 0.4 0.2 0.5 0.3 0.6 0.4 0.7 0.5 0.8 0.6 0.9 0.7 1.0 0.8 0.9 1.0

Water / Artiﬁcial cover

20,05 %

Water / BareArtiﬁcial soil / cover Dead vegetation

45,40 %

Bare Shrub / soil / Dead vegetation Grassland Shrub / Abundant / Grassland Vigorous vegetation Abundant / Vigorous vegetation Very abundant / Vigorous vegetation Very abundant / Vigorous vegetation

1,83 %

20,05 %

27,20 %

7,05 % 7,05 % 0,30 % 0,30 %

27,20 %

45,40 %

1,83 % 12,65 % 12,65 % 34,20 %

4,55 %

34,20 %

4,55 % 46,77 % 46,77 %

1 200 2 400 3 600 4 800 5 1000 6 1200 7 1400 8 1600 9 1800 10 2000 11 2200 12 2400 13 2600 14 2800 0m

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

SATELLITE

AutoML

INPUT

TOOL

A B C D E F G H I 1 2 3 4 5 6 7 8 9 10 11 12 13 14

K L

OUTPUT

LAYERS

COCONUTS

TARO PITS MANGROVES

taro pit pulaka

coconut

pulaka

coconut

shrub

RICH MAP

shrub

TYPOLOGIES

NDVI

taro pit

LAND USE

A B C D E F G H I 1 2 3 4 5 6 7 8 9 10 11 12 13 14

NDVI

K L

TYPOLOGIES

taro pit pulaka

coconut shrub

RICH MAP

pulaka

coconut shrub

TYPOLOGIES

NDVI

taro pit

LAND USE

06.1.1.2 F R E S H W A T E R Freshwater is an essential resource for human life to exist in a context. Freshwater is usually supplied through rivers, lakes, and the collection of rainwater. In this case study, we identified that freshwater was primarily supplied through the collection of rainwater into private collection tanks in every household. To identify the quantity of these tanks so that we can have an idea of the amount of water consumed the project uses Auto ML, a machine learning algorithm provided by google to identify these tanks using satellite images. The shape of the tank is used primarily to train the machine learning algorithm to make this identification.

Delegation of the European Union Water Most of Tuvalu’s population to beneﬁtted from increased household rainwater harvesting and storage capacities - with 1233 water tanks delivered and installed covering 86 percent of households in the outer islands. (as for 2013, and more after that) source : https://eeas.europa.eu/delegations/azerbaijan/2343/tuvalu-and-eu

3.45 m

3.28 m

ELEVATION

PLAN

WATER SYSTEM DIAGRAM

WATER TANK STATISTICS DIMENSIONS

r 3.45 m x h 3.28 m

MATERIAL

Polyethylene

CAPACITY

25000 litres

WATER TANKS USED TO LOCALLY STORE RAINWATER FROM ROOF RUN-OFF

e r

t) u source : International WeLoveU Foundation

source : PACC

source : International WeLoveU Foundation

tank

SATELLITE

AutoML

INPUT

TOOL

A B C D E F G H I 1 2 3 4 5 6 7 8 9 10 11 12 13 14

K L

OUTPUT

water tanks

LOCATION

06.1.1.3 C O N N E C T I V I T Y The project uses open street map data processed through the grasshopper component ELK to map out connectivity throughout the island. This data is also added to the rich map as a superimposed layer to analyze how the context is connected within and with the outside world.

OSM DATA

ELK

INPUT

TOOL

A B C D E F G H I 1 2 3 4 5 6 7 8 9 10 11 12 13 14

K L

OUTPUT

LAYERS

PRIMARY

RESIDENTIAL SERVICE PATH

TYPES

1 200 2 400 3 600 4 800 5 1000 6 1200 7 1400 8 1600 9 1800 10 2000 11 A 12

2200G

2400

13 2

2600

14 3 0m

200

400

PRIMARY

600

800

1000

1200

1400 4

2800 1600

1800

2000

2200

2400

2600

RESIDENTIAL SERVICE PATH

6 7 8 9

10 11

06.1.1.4 E N E R G Y Energy is usually supplied to a context via a centralized delivery system. However, sometimes this delivery system can be localized to each individual household. The energy provided to a context also heavily relies on how it is produced. The most common ways in which energy is produced are through fossil fuel generators and renewable energy sources. To identify the energy consumption and to map out the source of energy, the project takes on a more analog approach by referring to official documents that are published by government agencies. Which is then manually updated onto the rich map.

sources :

Global Sustainable Electricity Partnership

THE TUVALU SOLAR PANAL PROJECT www.globalelectricity.org/content/uploads/Tuvalu_Solar_Power_Project_FINAL.pdf

8 Tuvalu Electricity Corporation

Tuvalu Electricity Corporation

MASTERPLAN FOR RENEWABLE ELECTRICITY AND ENERGY EFFICIENCY IN TUVALU www.policy.asiapaciﬁcenergy.org/sites/default/-

ﬁles/master_plan_for_renewable_electricity.pdf 13

200

400

600

1 200 2 400 3 600 4 800 5 1000 6 1200 7 1400 8 1600 9 1800 SOLAR ROOFS

2000 11

DIESEL POWER STATION 2200

12 2400 13 2600 14 2800 0m

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

06.1.1.5 B U I L D I N G S Two approaches are used to collect data in this category. Firstly, Identifying the different footprints of the building through satellite images allowed the classification of three typologies: single building, administrative building, and social building consisting of 2-3 joined structures. The use of OSM data once again processed through the grasshopper plugin ELK helped verify this data to make it more accurate. Using these data collection methods to compliment each other helped mask lapses of accuracy in each method.

SATELITE IMAGERY

NERO

TYPOLOGIES

INPUT

TOOL

OUTPUT

ON SITE IMAGERY

YOLO

INPUT

TOOL

A B C D E F G H I 1 2 3 4 5 6 7 8 9 10 11 12 13 14

K L

OUTPUT

LAYERS

METAL SHEETS

CONCRETE WOOD

TYPES & LOCATION TYPES & LOCATION TYPES & LOCATION

1 200 2 400 3 600 4 800 5 1000 6 7

2 1

3 12

4 3 2 1 5 4 3 2 1

GOVERNMENT

11 12

GOVERNMENT PUBLIC

UNIVERSITY GOVERNMENT 0m

200 PUBLIC

400

UNIVERSITY GOVERNMENT CHURCH PUBLIC UNIVERSITY GOVERNMENT CHURCH PUBLIC HOTEL UNIVERSITY CHURCH PUBLIC HOTEL UNIVERSITY RESIDENTIAL CHURCH HOTEL UNIVERSITY RESIDENTIAL CHURCH HOTEL RESIDENTIAL CHURCH

HOTEL RESIDENTIAL HOTEL

600

800

1000

1200

G 1200

G 1400

G 1600

1800 G

2000 G

2200

B C D E F G H I J A 6 5 4 3 2400 2 1 7 6 5 4 2600 3 2 8 7 6 2800 5 4 3 9 1400 1600 1800 2000 2200 2400 2600 8 7 6 5 4 10 9 8 7 6 5 11 10 9 8 7 6 12 11 10 9 8 7 13 12 11 10 9 8 14 13 12 11 10 9 0m 200 400 600 800 1000 1200 1400 1600 1800 14 13 12 11 10 0m 200 400 600 800 1000 1200 1400 1600 1800 14 13 12 11 0m 200 400 600 800 1000 1200 1400 1600 1800 14 13 12

1 200 2 400 3 600 4 800 5 1000 6 1200 7 1400 8 1600 9 1800 10 2000 11 B

2200 G

12 1

2400

2600

13 14 3 0m

200

400

600

800

1000

1200

1400

2800 1600

1800

2000

2200

2400

2600

4 TYPOLOGY

Admnistrative & Public

5 6

TYPOLOGY

Merged buildings

7 8

TYPOLOGY

Single building

10 11 12

Social & Public T Y P O LO GY

200 m 2

200 m2

Wood 28,8 m3

Concrete

42,5 m3

60,3 m3

Metal 15 m3

Merged

T Y P O LO GY

200 m2

Single

T Y P O LO GY

85 m 2

85 m2

200 m2

Wood

26,8 m3

8 m3

Concrete Metal 15 m3

18 m3

Metal 4,5 m3

06.1.2 L I M I T A T I O N S The methodology derived from this project has some limitations when it comes to data collection. Some of these limitations are that the data collected vary in accuracy across the different categories while also when considering the building and material category generalized typologies mean lack of accountability for customized structure. Some of these limitations can be addressed via higher resolution satellite images from companies like MAXAR and European Space Imagery and also by using onsite data collection methods like aerial photography, street view photography, and surveys.

CATEGORIES

TOOLS

AGRICU�TURE

SSML

ELK

NERO

BUILDINGS

SSML

ELK

ANALOG

MATERIALS

SSML

ANALOG

ENERGY

ANALOG

CONNECTIVITY

SSML

ELK

WATER

SSML

ELK

ERO

LOG

L I M I TLAI T MIIOTNAST I O N S

ESTIMATED ESTIMATED ACCURACY ABOUT ACCURACY 75-80% ABOUT 75-80% CAN BE IMPROVED CAN BE IMPROVED WITH HIGH-RESOLUTION, WITH HIGH-RESOLUTION, MULTI-SPECTRAL MULTI-SPECTRAL IMAGERY IMAGERY

ACCURACY OF ACCURACY IDENTIFYING OF IDENTIFYING THE LOCATIONS THE LOCATIONS OF BUILDINGS OF BUILDINGS AND CATEGORISING AND CATEGORISING INTO DIFFERENT INTO DIFFERENT TYPOLOGIES. TYPOLOGIES.

ESTIMATED ESTIMATED ACCURACY ABOUT ACCURACY 70%ABOUT 70% GENERALISED GENERALISED TYPOLOGIES TYPOLOGIES MEAN LACKMEAN OF ACCOUNTABILITY LACK OF ACCOUNTABILITY FOR FOR CUSTOMISED CUSTOMISED STRUCTURES. STRUCTURES. CAN BE IMPROVED CAN BE IMPROVED WITH STREETVIEWS WITH STREETVIEWS AND FURTHER ANDWITH FURTHER ON-SITE WITH SURVEY ON-SITE SURVEY

IDENTIFYING IDENTIFYING THE LOCATIONS THE LOCATIONS OF RENEWABLE OF RENEWABLE AND AND NON-RENEWABLE NON-RENEWABLE ENERGY PRODUCTION ENERGY PRODUCTION FACILITIES FACILITIES

IDENTIFYING IDENTIFYING THE LOCATION THE AND LOCATION AMOUNT ANDOFAMOUNT WATER OF WATER TANKS ACROSS TANKS THEACROSS ISLANDTHE ISLAND

06.2 EVALUATION 06.2.1 T H E

S I M U L AT I O N

Data that is collected using the analysis is then translated into a rich map of the context in which each category is layered on top of each other. This rich map is then run against a sea-level rise simulation to analyze and estimate the loss of resources and land during a specific amount of time. This simulation helps the project define safety zones according to a given timeline to categorize the protocols that can be taken. This is to be used as a tool to continuously update and monitor the effects sea level rise has on the case study or any other context with the same issue.

06.2.2 G E N E R A L

S T R AT E G I E S

General strategies to mitigate the effects of sea level rise in a given context are divided into four different categories.

- Adapt - Retreat - Protect - Preserve

According to the analysis of data received from the algorithm as well as the external contextual forces, these strategies will specify which are the appropriate protocols that are to be implemented in a particular context. The project also looks at strategies at a micro level that can be implemented according to the analysis of data received from the algorithm and the need for it in a given context.

FLOATING CITY

ADAPT

SELF SUFFICIENT NEIGBORHOODS

BARRIER AROUND SITE

STRATEGIC RETREAT

COASTAL BARRIER

ABANDON

FLOOD PROOFING (BUILDING)

FLOOD PROOFING (SITE)

RETREAT

RAISED M

TREAT

RAISED MOUND

RAISED STILTS

AMPHIBIOUS STRUCTURE

FLOATING STRUCTURE

06.3

INTERVENTION

06.3.1 M E T H O D O L O G Y To come up with protocols to adapt the strategies to mitigate the effects of sea level rise the rich map that was developed was discretized into a cluster of 50mx50m. The population, amount of resources, and soil conditions are then analyzed in these individual clusters.

RICH MAP

DISCRETIZATION

ANALYZE SOIL CONDITIONS

POPULATION

IDENTIFY EMPTY SPACES

RESOURCES NEEDED PER PERSON (on average)

IMPLEMENT

RED

IDENTIFY RESOURCES RATIO

RICH MAP

SEA LEVEL RISE

SIMULATION

LOST RESOURCES

IDENTIFY SAFETY ZONES

1 2 3 4

REDISTRIBUTE

06.3.2 S T R A T E G I E S

ZONE IV ZONE III ZONE II ZONE I

IDENTIFYIDENTIFY SAFE ZONES SAFE ZONES

ZONE IV ZONE III ZONE II ZONE I

DISCRETIZE DISCRETIZE RICH MAPRICH MAP

ZONE IV

CATALOGUE CATALOGUE CLUSTERSCLUSTERS

buildings

water

vegetation vegetation energy energy

ZONE IV

ALLOCATEALLOCATE RESOURCES RESOURCES

ZONE IV

ZONE IV buildings

buildings

water

vegetation vegetation energy energy

Identify the safety zones on the rich map accordingly to topography and rising sea level

Discretize the rich map on a 50x50 m grid in order to identify the clusters

Catalogue the clusters accordingly to the resources ratio that they host and their safeness level

Within each cluster identify the empty zones where to allocate the resources

SLR

IDENTIFY

IDENTIFYSAFETY ZONES SAFETY ZONES

ZONE I

SAFETY ZONES 2021

ZONE II

ZONE III

2080

2050

SLR

ZONE I

SAFETY ZONES 2021 SLR

ZONE II

2050

2080

SAFETY ZONES

SLR

ZONE II

ZONE I

TY S

2021

2080

ZONE III 2050

ZONE II

2100

ZONEZONE IV III 2080

ZONE IV

2100

2120

N IDENTIFY EMPTY ZONES

IDENTIFY EMPTY ZONES

EMPTY SPACES

CONNECTIVITY

VEGETATION

BUILDINGS

06.3.3 P S E U D O

CODE

CONVERT

TYPE OF DATA FROM OSM VEGETATION

AREAS

Populate

BUILDINGS

BUILDING OUTLINES

Populate

ENERGY

POWER STATION OUTLINE

Populate

CONNECTIVITY

PATHWAY CENTER LINES

Populate

WATER

DATA POINTS

Populate

ISLAND (TOPOGRAPHY MESH) AVG. SEA LEVEL (BOX BREP) Identify safe zones (4) at 4 point in time

ISLAND MAP FROM OSM

Discretize in grid of 50m x 50m

I w s

Assign “safety scores” to each QUAD

BIOMORPHER Evaluate iterations with respect to this ration FITNESS VALUE Deﬁne a resource ratio for all QUADS

2 best iterations

2 best i

1 iteration to render as a game chips

100

T DATA TO POINTS 1 point for 10 sq. m 1 point for 1 sq. m 1 point for 1 sq. m 1 point for 1 m

PROJECT on TOPOGRAPHY

Evaluate safe resources with rising sea level

1 point for 1 water tank

ANEMONE

Identify resources in each QUAD with respect to safety zones and sea level rise

SIMULATE

MOVE TO THE : SAFEST QUAD (as per free spaces) CLOSEST SAFE QUAD (in next safe zone) (as per free spaces)

REALLOCATE Assign priorities for safe QUADS to accept these lost resources

Identify resources lost in each QUAD as the sea level rises

iterations

101

ZO I

N DISCRETIZE RICH MAP

50 m 50 m

0 100

500

2021

1000 m SLR

102

ZONE I

2021

00 m SLR

ZONE II

ZONE III

ZONE IV

2050

2080

2100

+ 0.95 m

+ 1.70 m

+ 2.40 m

103

ZONE I

Vegetation

ZONE II

Buildings

ZONE III

Freshwater

ZONE IV

Connectivity

Energy

2021

00 m SLR

104

2050

2080

2100

+ 0.95 m

+ 1.70 m

+ 2.40 m

Airport

ZONE I

Vegetation

ZONE II

Buildings

ZONE III

Freshwater

ZONE IV

Connectivity

Airport

Energy

2021

00 m SLR

2050

2080

2100

+ 0.95 m

+ 1.70 m

+ 2.40 m

105

ZONE I

Vegetation

ZONE II

Buildings

ZONE III

Freshwater

ZONE IV

Connectivity

Energy

2021

00 m SLR

106

2050

2080

2100

+ 0.95 m

+ 1.70 m

+ 2.40 m

Airport

06.3.4 R E D I S T R I B U T I O N

107

ZONE 3 ZONE 4 A

N 1

ZONE 3

ZONE 4

108

CLUSTER D

ZONE

ZONE 3 ZONE 4 B

CLUSTER

ZONE

ZONE 3

ZONE 4

BUILDINGS 2021

2050

2080

2100

+ 0.95 m

+ 1.70 m

+ 2.40 m

VEGETATION SLR

ZONE 3 ZONE 4 B

ZONE 3

ZONE 4

CLUSTER

780 sq.m

530 sq.m

600 sq.m

1000 sq.m

2200 sq.m

700 sq.m

200 sq.m

2100 sq.m

750 sq.m

110 sq.m

550 sq.m

1200 sq.m

2300 sq.m

3000 sq.m

800 sq.m

590 sq.m

60 sq.m

210 sq.m

970 sq.m

900 sq.m

2100 sq.m

1000 sq.m

380 sq.m

30 sq.m

430 sq.m

0 sq.m

1100 sq.m

2300 sq.m

2000 sq.m

1500 sq.m

650 sq.m

800 sq.m

0 sq.m

410 sq.m

4200 sq.m

1500 sq.m

2000 sq.m

BUILDINGS VEGETATION

ZONE

RESOURCES MOVED FROM ZONE

3125 sq.m BUILT UP 5 WATER TANKS UNITS 2500 sq.m AQUAPONICS

109

ZONE 3 ZONE 4 A

CLUSTER D

ZONE

ZONE 3

ZONE 4

BUILDINGS 2021

2050

2080

2100

+ 0.95 m

+ 1.70 m

+ 2.40 m

VEGETATION SLR

ZONE 3 ZONE 4 B

ZONE 3

ZONE 4

CLUSTER D

780 sq.m

530 sq.m

600 sq.m

1000 sq.m

2200 sq.m

700 sq.m

200 sq.m

2100 sq.m

750 sq.m

110 sq.m

550 sq.m

1200 sq.m

2300 sq.m

3000 sq.m

800 sq.m

590 sq.m

800 sq.m

500 sq.m

1600 sq.m

900 sq.m

2100 sq.m

1000 sq.m

380 sq.m

1000 sq.m

1500 sq.m

90 sq.m

1100 sq.m

2300 sq.m

2800 sq.m

1800 sq.m

650 sq.m

800 sq.m

870 sq.m

1600 sq.m

4200 sq.m

1500 sq.m

2000 sq.m

ZONE

BUILDINGS 2021

2050

2080

2100

+ 0.95 m

+ 1.70 m

+ 2.40 m

VEGETATION SLR

110

06.3.5

M A T E R I A L S

06.3.5.1 R E U S A B L E

BUILDING

The three typologies that were analyzed in the building analysis part of the project were deconstructed to the ideal material data rich models to map out the use of the material as a ratio against type and square foot area. These ideal models were created after observing and analyzing photographic and video material that was uploaded by individuals on the internet. The reusability of existing building materials is something that can be looked at for a resource scarce context like Tuvalu. By analyzing the building material against the sea level rise simulation, demolition can be pre planned and help maximize the reusability of the material already available on the island.

111

MATERIALS MATERIALS TYPOLOGIES TYPOLOGIES

METAL METAL SHEETS SHEETS ROOF ROOF

WALLS WALLS

STRUCTURE STRUCTURE

Con rein 18 WOOD WOOD

CONCRETE CONCRETE BLOCKS BLOCKS

CONCRETE CONCRETE

ROOF ROOF METAL METAL SHEETS SHEETS WALLS WALLS CONCRETE CONCRETE BLOCKS BLOCKS STRUCTURE STRUCTURE WOOD WOOD

CONCRETE CONCRETE

112

RATIO RATIO RATIO RATIO

STRATEGIES STRATEGIES STRATEGIES STRATEGIES

Metal Metal Metal Metal Wood Wood Wood Wood sheets sheets sheets sheets 1,5 m3 1,51,5 m3 m3 1,5 m3 structural structural structural structural 6 m36 m3 6 m36 m3 Concrete Concrete Concrete Concrete reinforced reinforced reinforced reinforced 18 m3 1818 m3 m3 18 m3

RE-USE RE-USE RE-USE RE-USE

RE-PROGRAM RE-PROGRAM RE-PROGRAM RE-PROGRAM

Concrete Concrete Concrete Concrete blocks blocks blocks blocks 20,520,5 m3 20,5 m3 20,5 m3 m3

RE-USE RE-USE RE-USE RE-USE

Concrete Concrete Concrete Concrete reinforced reinforced reinforced reinforced 6 m36 m3 6 m36 m3 Metal Metal Metal Metal sheets sheets sheets sheets 1,5 m3 1,51,5 m3 m3 1,5 m3

RE-USE RE-USE RE-USE RE-USE

Wood Wood Wood Wood structural structural structural structural 16 m3 1616 m3 m3 16 m3

RE-PROGRAM RE-PROGRAM RE-PROGRAM RE-PROGRAM

Concrete Concrete Concrete Concrete blocks blocks blocks blocks 20,520,5 m3 20,5 m3 20,5 m3 m3

RE-USE RE-USE RE-USE RE-USE

113

MATERIALS TYPOLOGIES

ROOF

METAL SHEETS

WALLS WOOD

STRUCTURE

WOOD

ROOF

METAL SHEETS

WALLS WOOD

STRUCTURE WOOD

CONCRETE

114

HEETS

RATIO RATIO RATIO

RATIO

STRATEGIES STRATEGIES STRATEGIES STRATEGIES

Metal Metal Metal Metal sheets sheets sheets sheets 1,5 m3 1,51,5 m3m3 1,5 m3 Wood Wood Wood siding siding siding 16 m3 16 16 m3m3

RE-USE RE-USE RE-USE RE-USE

Wood siding 16 m3

RE-USE RE-USE RE-USE RE-USE

Wood Wood Wood Wood structural structural structuralstructural RE-USE RE-USE RE-USE RE-USE 28 m3 28 28 m3m3 28 m3

HEETS

ConcreteConcrete Concrete Concrete reinforced reinforced reinforcedreinforced 8 m3 Metal 8 m3 8 m3 8 m3 Metal Metal Metal sheets sheets sheets sheets 1,5 m3 1,51,5 m3m3 1,5 m3

RE-USE RE-USE RE-USE RE-USE

RE-PROGRAM RE-PROGRAM RE-PROGRAM RE-PROGRAM

Wood Wood Wood Wood poor quality poor poor quality quality poor quality 16 m3 16 16 m3m3 16 m3

Wood Wood Wood Wood RE-CYCLE RE-CYCLE RE-CYCLERE-CYCLE structural structural structuralstructural 28 m3 28 28 m3m3 28 m3

RE-USE RE-USE RE-USE RE-USE

ETE

115

116

06.3.5.2 B A M B O O Bamboo was researched as a potential sustainable material for contexts such as Tuvalu. This is due to the versatility of the material and its ability to be harvested in cycles of every 4 years with almost no effort making it a very low cost material. Apart from these advantages bamboo is also low in weight with high composite and tensile strength. Bamboo is currently used for food, basketwork apart from construction. In construction, according to the age of the stem, it can be used for planks, strips, laths, as structural elements, for floors, and also as laminates. For this case study, we look to use the bamboo species, Guadua angustifolia. Primarily grown in tropical conditions, this species of bamboo grows up to 20m to 30m in height with a diameter of around 8 and 13cm which makes it quite suitable for construction.

117

BAMBOO Guadua angustifolia

HEIGHT Between 20 and 30 m DIAMETER Between 8 and 13 cm

118

P A

C IF

VAITUPU

NUKUFETAU

FUNAFUTI

NUKULAELAE

NUI

P A

C IF IC

C E A N

NIULAKITA

TUVALU

119

ADVANTAGES

RAPID GROWTH PRODUCTIVITY 1200 - 1400 stems per hectare/year

NON-POLLUTING AND RENEWABLE

SOIL It grows in sandy loam, loam and loamy sand. if the soil is too sandy compost can be added to retain the moisture

LOW WEIGHT

HIGH COMPRESSIVE AND TENSILE STRENGHT HARVESTING In 4 it is considered mature and it is cut

LOW COST

MANY APPLICATIONS USES ACCORDING TO AGE

120

FOOD

Today

30 days

BASKETWORK

1 year

PREVENTION PREVENTION Canes are immersed Canes arefor immersed for 4-6 days in a4-6 solution days inofa solution of borax and boric borax acid and boric acid

HIGH COMPRESSIVE HIGH COMPRESSIVE AND AND TENSILE STRENGTH TENSILE STRENGTH It is an eﬀective It is alternative an eﬀectivetoalternative wood to wood

Bamboo biomass Bamboo canbiomass be converted can be converted into energy or into fuel energy eitherordirectly fuel either directly or in form oforcharcoal, in form of pellet, charcoal, chips.pellet, chips.

- PLANKS - PLANKS - CIVIL STRUCTURES - CIVIL STRUCTURES - STRIPS - STRIPS - FLOORS - FLOORS ORK - LATHS - LAMINATES - LATHS - LAMINATES

2 years

4 years

121

122

CONCLUSIONS

123

CoRAAL 1.0 therefore expresses great potential for authorities of regions affected by sea level rise to evaluate and mitigate its effects. It also paves paths for various independent oportunities to develop information-gathering methodologies using state of the art technologies such as symantic and neural machine learning, computer vision, and artificial intelligence. CoRAAL 1.0 also facilitates these methodologies to be responsive to cater the unpredictability of natural conditions, thus making it reiterative and adaptive, and circular.

124

ZONE 3 ZONE 4 A

CLUSTER D

ZONE

ZONE 3

ZONE 4

BUILDINGS 2021

2050

2080

2100

+ 0.95 m

+ 1.70 m

+ 2.40 m

VEGETATION SLR

ZONE 3 ZONE 4 B

ZONE 3

ZONE 4

CLUSTER D

780 sq.m

530 sq.m

600 sq.m

1000 sq.m

2200 sq.m

700 sq.m

200 sq.m

2100 sq.m

750 sq.m

110 sq.m

550 sq.m

1200 sq.m

2300 sq.m

3000 sq.m

800 sq.m

590 sq.m

800 sq.m

500 sq.m

1600 sq.m

900 sq.m

2100 sq.m

1000 sq.m

380 sq.m

1000 sq.m

1500 sq.m

90 sq.m

1100 sq.m

2300 sq.m

2800 sq.m

1800 sq.m

650 sq.m

800 sq.m

870 sq.m

1600 sq.m

4200 sq.m

1500 sq.m

2000 sq.m

ZONE

BUILDINGS 2021

2050

2080

2100

+ 0.95 m

+ 1.70 m

+ 2.40 m

VEGETATION SLR

125

126

2100 VISION

127

128

129

130

REFERENCES

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