Design 2 | AA Em-tech | M. Arch | Ecologically sensitive tower cluster Design by Debolina Ray

Design II Debolina Rey - Maximo Tettamanzi Maria Luiza Gomez Torres

Abstract The aim of this work is to develop an urban project in the Hoo Pensisula able to fit 50.000 people, responding to the challenges this intertidal area presents in adapting to climate change. This holistic intervention not only considers the towers as buildings that serve as a habitat for humans, but also explores the possibilities of controlling and shaping the existing and future environment in order to create suitable conditions for a sustainable mariculture productive system. Over the next century climate change will cause sea level rise, and an increase in the storm surge frequency, changing the current conditions of the peninsula. The design proposes a network system able to adapt to environmental changes and population growth, consisting of tower costal defenses, tidal terracing, and clutch dikes that recreate proper conditions for mariculture. Through research of the site and computational analysis, key elements like tidal levels, predominant water directions, and fluid dynamics on site were understood in order to develop experiments that led to the proposed design.

Sea Level projection for year 2100

Content Abstract Introduction Research Design Process Design Proposal Conclusion References

Hard defense systems found at the coastline of the Hoo Peninsula

Introduction The area to be intervened is the Hoo Peninsula, located in Kent, England. Daily oscillation in sea level create a process of sedimentation that give place to high extensions of saltmarshes, habitat to native species of flora and fauna. The boundaries of the Peninsula are the Thames River in the North, and the Medway in the South, making this area susceptible to tidal and storm surge flooding. Historically several attempts to control water inflow inland through hard defense systems were applied. These suggests a futile effort to separate the land from the water, underestimating the potential the intertidal area has. These interventions often succeed in controlling water at an early stage but need to be improved in order to adapt to nature changes. Projections suggest that sea level will rise between 50 to 120 cm as a consequence of climate change. This will inevitably lead to the loss of the saltmarshes found in the area. The purpose of this project is to develop a more meaningful engagement with the sea, incorporating its several variables into a design proposal that far from denying or controlling it, perceives it as suitable place to inhabit. Furthermore, by controlling water velocity, depth, and sediment accretion, a suitable environment can be created for a sustainable mariculture productive system, while promoting the conditions for saltmarshes to thrive.

Research

Salt Marshes

A Salt Marsh is an ecosystem found in the area between land and open saltwater or brackish water. Areas with tides and low wave energy are exposed to a process of erosion and sediment accretion. With the ebbs, the land is covered by water containing soil particles that eventually settle creating a new layer of sediment when low tides occur. Once the proper conditions are given, saltwater tolerant pioneer species arrive binding the sediments with their root network and providing stability to the salt marsh. Marshes play a key role in dissipating wave energy and retaining water, acting as a natural costal defense that prevents shorelines from erosion. Furthermore, it is an ecosystem that hosts multiple animal and plant species.

12-

Several hard defense systems where introduced in the area. As seen in the above diagram, these barriers prevent salt marshes to grow inland, as the process by which saltmarshes are created is restricted. Additionally, as a consequence of global warming, sea level has begun to rise affecting coastal areas and having a negative impact on salt marshes as they are eroded by floods until they disintegrate.

Diagrams 1, 2 and 3 show the natural process by which salt marshes are formed due to sediment accretion. Diagram 4 shows the abrupt cut off the natural process due to the building of the sea wall located along the cost. Diagram 5 and 6 explains the two main consequences of this intervention: the first one being the sinking of the inland and the loss of saltmarshes. The second being the continuous rise in the sea, forces humans to keep increasing the height of the wall, leading to loss of natural habitats along the shore.

Environmental Factors One of the features that make the Hoo Peninsula particular is the fact that it is located in an intertidal zone. This area is submerged twice a day due to an increase in the sea level, as a consequence of gravitational forces applied by the moon and soon (having the moon a greater influence because of its proximity to Earth). The position of these bodies combined with the rotation of Earth pull water bodies in different directions, causing sea level to rise and fall, with the ebbs and flows respectively. Tide tables are used to predict tides time, tide levels and tidal ranges in a specific location, anticipating, in the case of the Hoo peninsula, the height and time of the two high and low tides of each day. However, these predictions are based on the position of the sun and moon according to the Earth, and factors like wind direction, precipitation and atmospheric pressure might influence these predictions. We gathered multiple environmental information in order to overlap it with the highest of the high and lowest of the low tides found in each month, predicted for 2019. We were interested in understanding if there was any correlation between environmental factors that could be later used as an input for the design proposal. From inside out: . The first ring plots the lowest of the low and the highest of high tides recorded in each month. The dotted line is the Mean Sea Level. . The second ring shows the average high and low temperature for each month, being the dotted line 0 C degrees. . The grey bars show the average rainfall in mm. . The colored bars indicate the average speed of wind gusts and the predominant wind direction. It can be observed that tidal ranges are higher during summer and winter months, when compared to in between seasons. This is mostly affected by the low tides which increase considerably during Spring and Autumn, whereas high tides values are considerably even throughout the year. To plot an example, the lowest of the low tides of November was 70 cm and the highest of the high tides was 600 cm. If compared to February, the lowest of the low tides was 0 cm while the highest was 610 cm. The predominant wind direction throughout the year is South West, which we hypothesize helps diminish the wave energy that flows inland from the Thames River. Rainwater fall happens all year round quite evenly, except for winter months where it decreases, particularly in February. Even though rain fall does not have a meaningful impact on tides, the information gathered could be useful for our design proposal. Fresh water cannot be found in the area, as both Medway and Thames rivers have relatively high saline levels by the time they reach the Hoo Peninsula. The amount of rainwater throughout the year suggests that its collection could be an option for obtaining fresh water for multiple uses. Another important factor to incorporate to the design proposal is Storm Surge. Storm Surge is a rise in the Seal Level that occur during intense storms. In the Hoo Peninsula, this phenomenon happens approximately once every 100 years, and represents an increase of 3 meters above the high tide level.

Climate Change

Global warming is the rise in the atmosphere temperature, caused by an increase in the levels of Carbon Dioxide among other pollutants causing the well know Green House effect. This impacts sea level rise in two ways. First, as the temperature increases, water expands as it warms. Furthermore, the melting of glaciers and ice sheet adds water to the existing water bodies. Projections suggest that the rise in the sea level will be between 20 to 120 cm. The diagrams above show how the area to intervene will be affected by sea level rise. The first diagram portrays how the area is nowdays, and remaining three show future projections of flooded regions by storm surge in years 2050, 2075 and 2100.

Additionally, it is estimated that storm surge frequency will increase, also as a consequence of global warming. The section illustrates the different tides predicted for 2019, considering the storm surge and sea level rise. Having a clear understanding of these levels will be key in developing our design strategy, as we consider our starting point to be the fact that a vast part of the area will be flooded, and the existing ecosystem lost.

Flora and Fauna

Three biomes were identified in the Hoo Peninsula area: saltmarshes, mudflats and estuaries. Different plant and animal species thrive in each of these unique environments. After identifying local species, an analysis was carried to understand the relationship between salinity and depth- height, required for these species to survive. Regarding Flora found at estuaries, the salinity levels tolerated is between 15 to 35 PSU. It was observed that algae vary in color according to the depth at which they are located, as they absorb different spectrums of sunlight. On the other hand, Fauna wise, we can find several fish species, most of which are suitable for human consumption. Flora in Mudflats consist of Phytoplankton and bacteria, that feed on nitrogen and sulfur, helping saltmarshes to be more fertile. Furthermore, animals found consist of warms, bivalves and snails. Lastly, saltmarshes have three groups of plant species: pioneers, lower marsh and middle marsh species. Pioneers have high salinity tolerance as they are submerged during high tides, opposite to middle marsh species who have low salinity tolerance and live at higher heights. Regarding fauna, most species are crustaceous and some fishes.

Flora

Fauna

Integrated Multi Trophic Aquaculture system

Traditionally, the most common way of getting sea life species for human consumption was through capture, often leading to overfishing and threatening the balance of multiple ecosystems. Aquaculture production is an alternative to open sea fishing; however, it is not always environmentally conscious. Closed containerized monoculture is resource intensive and produce nutrient pollution that also has negative impact on the marine environment. Integrated multi trophic aquaculture (IMTA) is a way of mariculture, were multiple species play a different role in the energy cycle, feeding on each other’s waste. This system has several advantages like reducing nutrient pollution in water, increasing productivity, improving water quality and reducing wave energy. Unlike monoculture, where flow of energy is linear (input food - output waste), IMTA is cyclical and a less wasteful model, making it more ecologically efficient.

A typical IMTA system consists of 4 species. The primary agent [1] is usually fed by processing waste of commercial fish. They are the ones that produce the primary waste that will serve as input for the other species to consume. Their waste is categorized into inorganic nutrients and organic particulates. Suspension feeders [2] are usually bivalve mollusks that consume both organic and inorganic solids. They are cultivated either in baskets, or along a rope attached to a structural element. Seaweeds [3] are nutrient bio extractors that feed on inorganic nutrients expelled by the primary and secondary agents. They require a solid surface to attach to, and sunlight for photosynthesis. Some seaweed species can serve both as food and as a source for biofuel. Finally, deposit feeders [4] consume organic particulates that settle to the sea floor. Even though they can be used as a source of human food, just some Asian cultures consume them. They can also be used for medical purposes.

In order to introduce IMTA to our design proposal we researched on the energy cycle and food chain of the species found in the area. This enabled an understanding of the trophic role each specie has, in order to select and incorporate them to the mariculture system.

The design proposal aims to introduce 50.000 people in the area. This new city will produce effluent and waste, that can be treated in such a way that will substitute the primary agent of the IMTA system. Storm surge and sewage can be treated and transformed into the organic and inorganic particulates suspension feeders, seaweed and deposit feeders can feed on. Our aim is to incorporate the city to the mariculture system, reducing the waste by utilizing it as the primary food engine of the system. Regarding suspension feeders, the species introduced are Ostrea Edulis and Mytillus Edulis. Oysters need a depth of around 2 meters to live and need a controlled water velocity of 0.5 m/s. Mussels are grown in ropes that can be attached to a structural element. Concerning seaweed, the aim is to incorporate Porphyra Umbilicalis and Saccharina Lattisima. Finally, for the deposit feeders, the selected species are the Zostera Marina and Ocnus Planci.

IMTA Infraestructure In order to create suitable environments for the different species, several infrastructure systems were studied. Mussel Posts are ropes attached to structural elements such as columns. Mussels do not need a specific depth to thrive, making them suitable for almost every intertidal gradient. The posts when placed closed to each other, act as a barrier that also help slow the water velocity as it passes through the path. Seaweed lines are a way of cultivation in which rows of long nets are positioned in multiple rows, where seaweed is able to attach to in order to grow. Due to its configuration, they help mitigate wave energy.

Tidal terraces are low walls positioned in the intertidal area, at different distances from the coastline. Their aim is to retain sediment in order to restore saltmarsh formation. The terraces at different heights offer the possibility to grow species that drive at specific intertidal level. A Clutch Dike is a wall that retains water. With the ebbs, sea water flushes inside the pond, while with low tides, the pond remains inundated. We aim to introduce this infrastructure in order to create ponds of a specific height for oyster cultivation.

Design Process

1.1 DESIGN HYPOTHESIS In order to achieve a suitable environment where different plant and animal species could grow within our IMTA system, we needed to control: . Water velocity as Oysters thrive at 0.5 m/s. . Water turbidity . Depth as Oysters live at certain profundities Based on sea level rise projections, the Hoo Peninsula area will be covered by water. Our hypothesis is that by introducing inhabitable buildings at strategic locations, these would act as coastal defenses that would reduce the velocity of water flowing inland. By selecting the low velocity areas for ponds to be allocated, other soft engineering flood management techniques could be introduced to create a suitable environment for the species to be cultivated. A network will later be developed in order to merge the residential and the productive systems into one.

IMTA ( INTEGRATED MULTI TROPHIC AQUACULTURE)

FLOOD MANAGEMENT TECHNIQUES

[1] Primary Fed Agent that produce primary waste stream [2] Molluscs to consume suspended organic materials

[a] Clutch Dikes - to achieve a system of ponds for Oyster cultivation

[3] Sea Weeds to assimilate dissolved nutrients

[b] Tidal Terracing - Improve Water Quality and help in natural growth of marshes, while contributing to reduce water velocity.

[4] Deposit Feeders to ingest settleable solids

[c] Coastal Defence Towers - Reduce Water Velocity

Tidal terracing with naturally growing marshes Clutch dikes ponds that oysters

to form cultures Water flow direction

Coastal defenses with mussel posts + habitable towers above the podium level

[1] [a + b]

[3 + 4] [a + b]

[2] [c]

1.2 RELATIONSHIP OF HABITAT GENERATION V.S. POPULATION In order to accommodate the projected 50.000 people in the provided site, we rationalise the total population to fit into towers. We decide to accommodate 15 people per floor plate area per tower, where we allocate 20 sqm. as minimum habitable area per person. Taking a basic projection of 40 floors per tower to accommodate 600 people per tower, we also decide to define a tower cluster with minimum number of towers with an urban hub at the center of the cluster. Each tower cluster has a minimum of 5 towers which accommodates a minimum of 3000 people that have access to the urban hub at the center of the cluster.

people / floor 300 sqm

600

people / tower

3000

people / cluster

Ground Podium Area = 300 sqm + 50% of 300 (open space areas) = 450 sqm = 20 x 25 m

For the podium that also acts as the refuge area, the floor area is a 50% addition of the 300 sqm allocated per floor area in order to have enough open space for people to access it and utilise it as a meeting or gathering spot.

1.3 STRATEGY FOR REDUCING WATER VELOCITY Width x

[2.0]

Lenght y

Existing data on principles of geometry and water flow regulation have been tested by previous Emtech student, which states a range of values within which if the coastal defense (with a particular geometry) are positioned and spaced out, acts as a collated system that is effective in reducing the overall water velocity passing through those geometries. Image ref: Thesis name, pg no.

Area = 113 sq.m

[2.0]

Linear Dimension Arrangement 0.94 x

0.47 x

1.42 x

1.90 x

[2.0]

Linear 2-Dimension Arrangement

1.42 x

1.72 y

1.37 y

1.03 y

0.69 y

0.34 y

2.36 x

1.42 x

Linear Dimension Arrangement X Distance

Y Distance

- ve P (Pa) + ve P (Pa)

Max. Velocity (m/s)

0.47 x

- 197849

88903

7.9

0.94 x

- 16175.5

20545

8.29

1.42 x

- 15882.2

20638

7.5

1.90 x

- 16129.3

22021.2

7.8

2.36 x

- 152631

19883.2

7.64

Table 1

Linear 2-Dimension Arrangement X Distance

1.42 x

Y Distance

- ve P (Pa) + ve P (Pa)

Max. Velocity (m/s)

0.34 y

- 145546

19224.8

7.3

0.69 y

- 14962

21065.7

7.11

1.03 y

- 14544.4

19594.9

6.95

1.37 y

- 15394.2

19504.2

6.96

1.72 y

- 15910.0

20616.3

8.2

Table 2

Hence, we performed a few cfd tests on various shapes with varying positions and spacings in order to choose our tower defenses which fit our criteria of both function and aesthetics.

Test 1 is done using a set of 3 circular geometries extruded as a solid of diameter - 2m, spaced equally with 2m gap between each other. The results show that it is capable of reducing water velocity right behind each geometry, but increased water velocity in between each other from 1.5 m/s to 2.2m/s. Test 2 is done using a set of 6 circular geometries extruded as a solid of diameter - 2m, spaced equally with 2m gap between each other. The results show that it is capable of reducing water velocity right behind each geometry but does not create any distinct low velocity zones/areas, and at the same time increased water velocity in the gaps between each other from 1.5 m/s to 2.6 m/s.

Test 3 is done using a set of 5 circular geometries extruded as a solid of diameter - 2m, spaced equally with 2m gap between each other. This time the top row is equally offsetted with respect to the first row in the horizontal axis. The results show that it is capable of reducing water velocity right behind each geometry, with larger distinct areas of low water velocity. Test 4 is similar to test 3, changing only the gap sizes between each shape from 2m to 4m. The results show that it is not very effective at generating any distinct low water velocity zones/areas, and starts acting as a singular component rather than a collated one.

Test 5 is done keeping the basic geometry areas same, but changing the component shape to a tear drop. The results show that it is capable of reducing water velocity right behind each geometry, with larger distinct areas of low water velocity. Test 6 is done keeping the basic geometry areas same, but changing the component shape to a tear drop and also elongating the tail. The results show that it is capable of reducing water velocity right behind each geometry, with larger distinct areas of low water velocity, but also effectively channels the water through them in a better fashion.

Finally, test 7 is done keeping the basic setup same as test 5 but introducing diamond shape as geometry. Results show that it is equally effective in reducing water velocities as compared to tests 3 & 5,

Hence, keeping in mind the tower morphology which we were interested in developing, we chose diamond shape as the podium shape as it suited our design function criteria and aesthetic sensibilities. Further on, since we were interested in introducing the mussel posts as the lowest parts of the coastal defenses for mussel culture, we tested a simpler prototype with diamond flat slab and circular posts as columns in cfd to test if it had the same efficiency as that of the previous solid form tests done from test 1 to test 7,

Status: Transient Analysis: 3D Wind Speed: 1.500 (m/s) Lenght: 0.151 (m) Width: 0.071 (m) Height: 0.023 (m) Voxel size: 0.023 (m)

Results of test 8 show that the prototype works equally well in channeling the water flow and reducing the water velocity and generate a distinct area behind it, which would be suitable enough for the culture of mariculture ponds

Diagram showing a similar result using no of columns v.s. solid wall in CFD setup

1.4 WORK FLOW BASED ON THE HYPOTHESIS In order to test the hypothesis, we set out a flowchart that guides the algorithm building for 2 distinct goals: 1. Network generation. 2. Tower morphology generation. The steps taken are elaborated further ahead in the document.

1.4.a

CFD on existing terrain

1.4.b

1.4.c

Extraction of areas showing highest values of water velocity

Coastal Defence size and separation as per area calculations per tower

1.4.d

CFD on areas with coastal defenses placed in the high water velocity regions

1.4.e

1.4.f

1.4.h

Connection hierarchy b/w hub, towers & ponds to higher ground

Placement of ponds in the low velocity regions

1.4.g

Develop tower as per range of people to accomodate

1.4.i

Network Generation : Create resolved connections within the system elements to generate a network. Run a G.A. for the network and select the optimum candidate.

Run G.A. to select a range of best solutions

1.4.j

Populate with the tower options on the coastal defenses

1.4.a CFD on existing terrain

A CFD analysis was performed in the area, in order to simulate how water behaves in a flooding scenario. We observed that there are multiple yellow regions that represent those with high water velocity.

20 M X

30 M 1.5 X

37.5 M 1.5Y

25 M

30 M 1.5 X

Implantation Strategy 1.

Extract segment of curve exposed to the first hit of water flowing inland.

Offset curve based on the distance on y direction.

Divide curves according to the distance in x direction.

Place costal defenses in points.

Apply same strategy to all the costal defense areas detected.

1.4.d Second CFD test with new placement of coastal defenses

First CFD with costal defenses

Second CFD with costal defenses

Zoom of CFD of group 1

Zoom of CFD of group 2

In order to test our hypothesis a CFD analysis was carried at a global scale. The results were not as expected, and our hypothesis could not be proven right or wrong, due to the fact that the resolution was inadequate. In order to have accurate results, we conducted tests at local scale, analyzing groups 1 and 2 separately. The results show clear dark blue areas that indicate where the velocity of the water is low, proving the system works in efficiently controlling the speed of the flow.

1.4.e Placement of ponds in the low velocity regions

Diagram showing a similar result using no of columns v.s. solid wall in cfd

X X

X X X

X X

Diagram showing a similar result using no of columns v.s. solid wall in cfd

By having the initial relative position of the costal defenses, we obtained the low water velocity regions, shown as blue areas in the CFD. After that, the regions located after the costal defense system were selected in order to position the IMTA ponds. These regions were populated with points in order for a future GA be able to select the best position according to the tower/ponds ratio proposed.

Strategy for ratio of number of pond v.s. number of towers in vicinity

In order to understand the corellation between the number of ponds that require to be generated per blue areas, we based it on the number of places allocated for workers per tower. We calculated the same by hypothising the number of workers required for every element of the IMTA system to function.

600

people / floor 300 sqm

3000

people / tower

We made a rough estimation of the number of oysters that could be reared per sq m, which is 20. With this information, we calculated the surface area of each IMTA pond which came to be roughly around 1500 sq. meters.

people / cluster

Ground Podium Area = 300 sqm + 50% of 300 (open space areas) = 450 sqm = 20 x 25 m

1500 x 20 oysters per sqm = 30000 oysters Hence, we rationalised that 1 person tending to a quantity of 1500 oysters per pond could suffice. Hence, a total of 20 people per pond were allocated.

Similarly, we hypothised 2 workers living per building could take care of the floating sea weed pods and mussel s being reared on the columns beneath the podium of their tower.

Hence, we concluded that a total of 20 people, where 2 workers staying per building would be able to run the complete IMTA mariculture system, we allocated 10 buildings to 1 pond ratio, in order for the algorithm to pick pond numbers in the low water velocity regions within the proximity of each coastal defense zone.

people / floor 300 sqm

15 600

people people / floor / tower 300 sqm

15 600

3000

15 600

3000

15 600

3000

600

3000

people people / floor / tower people / cluster people people / floor / tower people / cluster people people / floor / tower people / cluster people / tower people / cluster 300 sqm 300 sqm 300 sqm

3000

people / cluster

Ground Podium Area = 300 Ground sqm +Podium 50% of Area 300 (open = 300 Ground space sqm +Podium areas) 50% of Area 300 (open = 300 Ground space sqm +Podium areas) 50% of Area 300 (open = 300 Ground space sqm +Podium areas) 50% of Area 300 (open = 300 space sqm +areas) 50% of 300 (open space areas) = 450 sqm = 450 sqm = 450 sqm = 450 sqm = 450 sqm = 20 x 25 m = 20 x 25 m = 20 x 25 m = 20 x 25 m = 20 x 25 m

1 IMTA pond system 15

people / floor 300 sqm

15 600

people people / floor / tower 300 sqm

15 600

3000

15 600

3000

15 600

3000

600

3000

people people / floor / tower people / cluster people people / floor / tower people / cluster people people / floor / tower people / cluster people / tower people / cluster 300 sqm 300 sqm 300 sqm

3000

people / cluster

Hence, we calculate the number of tentative towers for every clustered zone and number of ponds placed in the low water velocity areas ( blue regions ) vary in the range of 1 to 3. E.g. if there are more than 10 and less than 20 towers in a clustered zone, 1 random point out of the point population in the region gets picked and designates that very point as the position for the pond placement.

1.4.f Connection hierarchy between hub, towers & ponds to higher ground For the network generation strategy, we approached the connection logic from a micro to macro scale. We formulated the bridges based on the shortest path for a human to move between hub and local cluster, one hub to another, one coastal defence cluster to another, closest urban hub to the closest IMTA ponds, connections between closest lying ponds and the shortest connections between the habitat on Medway and Thames estuary sides with the higher grounds on isle of grain and hoo region

1. Radial connection between urban hub and towers around

LEGEND Residential towers

In context

Urban Hub

Platforms

Connections

higher ground

IMTA ponds

2. Linear connection between all urban hubs within each coastal defense cluster zone.

3. Linear connections between all coastal defense cluster zone via the closest urban hubs.

5. Linear connections between all the IMTA ponds within each low water velocity zone near a coastal defense zone.

4. Linear connections between the Imta ponds in the vicinity of a coastal defense cluster to the closest urban hub. 6.

6. radial connection from floating platforms to the highest ground and the coastal defense clusters on thames and medway estuary.

1.4.g Network Generation : G.A Setup : After creating the major connections as per the network hierarchies mentioned in section 1.4.g Body part - A Ponds

Body part - B Urban Hub

Body part - C Residential Tower cluster

Body part - D Platforms

HYPOTHETICAL FUTURE RAILWAY STATION

BODY PLAN

GENE REGULATION : There are 4 types of gene regulation affecting the body parts, such as change in pond position being appointed onto the random populated points in the low water velocity zones, change on the urban hub vs tower positions within a range of values for gaps from 0.5x and 0.5y to 2x and 2y (as mentioned in section 1.2), change in platform positions along the central line joining the higher grounds within a range of division of points, and change in line position of the central line itself - varying along the contour edges of the higher grounds on either sides.

Change in pond position

Change in platform position

Change in tower and hub positions

Change in road position

FITNESS CRITERIA AND EVALUATION FACTORS : The diagrams below show the projected changes expected due to the genes regulations influenced by each fitness criteria. FC1 - Maximize distance between neighbouring ponds

FC2 - Maximize distance between towers

FC4 - Minimize distance between ponds, road and platform

Ponds - Platform

Platform - Highland

FC3 - Minimize distance between pond and central hub

FC5 - Minimize overall path lenght amongst all clusters

Highland - Transportation Hub

Minimize Distance

All in all, the Fitness criterion 1 and 3 work against each other. Fitness criterion 4 is affected by 3 out of the 4 gene regulations, which is thus expected to find a middle ground based on the outcomes of the body part positions influenced by fitness criterion 1, 2, and 3. Fitness criterion 5 is introduced with the intention for the central path length to stay more central between the higher grounds and the habitats on either sides of the marshlands. For the G.A., we ran the simulation for a generation count of 100 and generation size as 20, making the population count 2000 in order for us to be able to observe better possibilities and variation in the solution set gained from the setup.

The diagram below shows how the 4 genes affect each fitness criterions.

Fitness criterias FC1 - Maximise distance b/w neighbouring ponds

Genes

Moving body part a in X and Y direction

Moving body part b in X and Y direction

FC2 - Maximise distance between towers

Move body part c in X and Y direction

Move body part d in X and Y direction

FC3 - Minimise distance b/w pond and central hub

FC4 - Minimise distance between ponds, road and platform

FC5 - Minimise overall path length

amongst all clusters

FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

Gen: 99 | Ind: 7

Gen: 99 | Ind: 19 FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 3 : 3533.411226

G.A. RESULTS : FV. 4 : 11319.216266 Gen: 99 | Ind: 12

Gen: 99 | Ind: 12

FV.15: :1.9082e-6 18019.240877 FV. FV. 2 :99 7.8902e-7 Gen: | Ind: 5 FV.1 3: 1.8342e-6 : 3683.042066 FV. FV.2 4: 7.8282e-7 : 10261.362468 FV. FV.3 5: 3305.168806 : 18248.105848 FV.

FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 Gen: 99 | Ind: FV. FV.31: 3683.042066 : 1.8342e-6 FV. FV.42: 10261.362468 : 7.8282e-7 FV. FV.53: 18248.105848 : 3305.168806

FV. 4 : 10778.281182 FV. 5 : 18507.058042

FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 4 : 10245.734877 99 | Ind: 13 99 | Ind: 16 Gen: FV. 1 : 1.9396e-6 FV. 5 : 17964.637644 FV. 2 : 7.8021e-7 FV. 1 : 1.9252e-6 FV. 3 : 99 4301.443093 Gen: | Ind: 6 FV. 2 : 7.8228e-7 FV.1 4: 1.8804e-6 : 10475.51295 FV. FV.2 5: 7.8317e-7 : 17884.547923 FV. FV. 3 : 3784.52184 FV. 3 : 4033.774968 FV. 4 : 10245.734877 FV. 4 : 10441.052915 FV. 5 : 17742.995853 FV. 5 : 17964.637644

Pareto Fronts in the last generation :

Gen: 99 | Ind: 13

FV. 1 : 1.9396e-6 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 Gen: 99 | Ind: FV. FV.4 1: 10475.51295 : 1.8804e-6 FV. FV.5 2: 17884.547923 : 7.8317e-7

FV. 3 : 4033.774968 FV. 4 : 10441.052915 FV. 5 : 17742.995853

FV. 1 : FV. 1.9005e-6 1 : 1.9162e-6 FV. 2 : 7.7664e-7 FV. 2 : FV. 7.818e-7 3 : 4491.32147 Gen: 99 | Ind: 7 41 : 10005.035926 FV. : 1.8613e-6 FV. 3 : FV. 3862.988455 FV. 52 : 17847.172774 FV. : 7.9567e-7 FV. 4 : 10096.553409 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18116.625625

FV. 1 : 1.9162e-6 FV. 2 : 7.7664e-7 FV. 3 : 99 4491.32147 Gen: | Ind: 7 FV.14: 1.8613e-6 : 10005.035926 FV. FV.25: 7.9567e-7 : 17847.172774 FV. FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

FV. 5 : 18163.916349

FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 Gen: 99 | Ind: 17 FV. 5 : 18116.625625 FV. 1 : 1.9397e-6

Gen: 99 | Ind:

FV. 1 : 1.9496e-6 FV. 2 : 7.8076e-7 FV. 3 : 4175.840713 FV. 4 : 9964.877256 FV. 5 : 17927.484914

Gen: 99 | Ind: 9

Gen: 99 | Ind: 14

We extracted the pareto fronts from the last generation which shows a lot of variation in terms of optimum solutions.

Gen: 99 | Ind: 0

Gen: 99 | Ind: Gen:8 99 | Ind: 17

FV. 1 : 1.9206e-6FV. 1 : 1.9005e-6 FV. 2 : 7.7862e-7FV. 2 : 7.818e-7 FV. 3 : 4528.158705 FV. 3 : 3862.988455 FV. 4 : 10005.035926 4 : 10096.553409 Gen:Gen: 99 | Ind: 17 FV. Ind: 14 FV. 99 5 :| 17816.09262 FV. 5 : 18116.625625

Gen: 99 | Ind: 17

Gen: 99 | Ind: 16

FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 99 | Ind: FV. 4Gen: : 10245.734877 FV. 1 : 1.9496e-6 FV. 516 : 17964.637644 Gen: 99 | Ind: FV. 2 : 7.8076e-7 FV. 1 : 1.9252e-6 FV. 3 : 4175.840713 FV. 2 : 7.8228e-7 FV. 4 : 9964.877256 FV. 3 : 3784.52184 FV. 4 : 10245.734877 FV. 5 : 17927.484914 FV. 1 : 1.8656e-6 FV. 5 : 17964.637644 FV. 2 : 7.836e-7

FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

Gen: 99 | Ind: 16

FV. 1 : 1.8613e-6 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349 Gen:

FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

Gen: 99 | Ind: 9

FV. 3 : 3186.31059 FV. 1 : 1.8656e-6 Gen: 99 | Ind: 18 FV. 2 : 7.836e-7 FV. 4 : 10792.269907 FV. 1 : 1.869e-6 FV. 3 : 3186.31059 Gen: 99 | Ind: 18 FV. 5 : 18673.159006 FV. 2 : 7.9318e-7

Gen: 99 | Ind: 0

Gen: 99 | Ind: 14

FV. 1 : 1.9162e-6 Gen: 99 | Ind: FV. 2 : 7.7664e-7 FV.31: :4491.32147 1.8613e-6 FV. FV.42: :10005.035926 7.9567e-7 FV. FV.53: :17847.172774 3133.120874 FV. FV. 4 : 10989.167861 FV. 599 : 18163.916349 Gen: | Ind: 0

FV. 1 : 1.9496e-6 FV. 2Gen: : 7.8076e-7 99 | Ind: 18 FV. 3FV. : 4175.840713 1 : 1.869e-6 FV. 4FV. : 9964.877256 2 : 7.9318e-7 Gen: 99 | Ind: 7 FV. 5FV. : 17927.484914 3 : 3273.610832 FV. 1 : 1.8613e-6 FV. 2 : 7.9567e-7 FV. 4 : 10914.866979 FV. 3 : 3133.120874 FV. 5 : 18037.302474 FV. 4 : 10989.167861

Poor ranking in fc 1 and 3

Gen: 99 | Ind: 15 Gen: 99 | Ind: 8

FV. 5 : 18163.916349 Gen: 99 | Ind: 0

FV. 1 : 1.9496e-6 FV. 2 : 7.8076e-7 FV. 3 : 4175.840713 FV. 4 : 9964.877256 FV. 5 : 17927.484914

Gen: 99 | Ind: 18

FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 Gen: 99 | Ind: FV. 4 : 10914.866979 FV. 1 : 1.896e-6 FV. 5 : 18037.302474 FV. 2 : 7.8753e-7

FV. 1 : 1.8766e-6 FV. 2 : 7.849e-7 Gen: 99 | Ind:

FV. FV. 31 :: 3605.589886 1.9206e-6 FV. FV. 42 :: 11186.817572 7.7862e-7 FV. FV. 53 :: 17990.597995 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262 Gen: 99 | Ind: 1 FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7 FV. 3 : 3934.001996 FV. 4 : 11522.452081 FV. 5 : 17848.236866

Poor ranking in fc 1 and 4

Gen: 99 | Ind: 1

FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 11

FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 Gen: 99 | Ind: 16FV. 5 : 18286.02663 FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 4 : 10245.734877 Gen: 99 | Ind: 9 FV. 51 : 17964.637644 FV. : 1.8656e-6 FV. 2 : 7.836e-7 FV. 3 : 3186.31059 FV. 4 : 99 10792.269907 Gen: | Ind: 2 FV.1 5: 1.896e-6 : 18673.159006 FV. FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 3

Gen: 99 | Ind: 2

Gen: 99 | Ind: 16 FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 4 : 10245.734877 FV. 5 : 17964.637644

FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 17 FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

Gen: 99 | Ind: 11

FV. 1 : 1.8432e-6 FV. 2 : 7.8277e-7 Gen: 99 | Ind: FV. 311 : 3420.160779 : 1.8432e-6 FV. 4 : 11194.183761 FV. 2 : 7.8277e-7 FV. 5 : 18596.835547 FV. 3 : 3420.160779 FV. 4 : 11194.183761 FV. 5 : 18596.835547

Gen: 99 | Ind: 2

Gen: 99 | Ind: 3

Poor ranking in fc 2, 4 and 5.

FV. 1 : 1.9541e-6 FV. 2 : 7.7622e-7 FV. 3 : 4172.860084 FV. 4 : 10555.452941 FV. 5 : 17991.902887

FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 1 FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 17 FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

Gen: 99 | Ind: 10

FV. 1 : 1.8584e-6 FV. 2 : 7.8365e-7 Gen: 99 | Ind: 3 FV. 3 : 3360.856862 FV. 1 : 1.9541e-6 FV. 4 : 10646.820418 FV. 2 : 7.7622e-7 FV. 5 : 18296.133586 FV. 3 : 4172.860084

Poor ranking in fc 1 and 3. FV. 4 : 10555.452941 FV. 5 : 17991.902887

FV. 1 : 1.9541e-6 FV. 2 : 7.7622e-7 FV. 3 : 4172.860084 FV. 4 : 10555.452941 Gen: 99 | Ind: FV. 5 : 18 17991.902887 FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. 4 : 10914.866979 FV. 5 : 18037.302474

Gen: 99 | Ind: 4

FV. 1 : 1.9615e-6 FV. 2 : 7.7955e-7 FV. 3 : 4263.600798 FV. 4 : 9970.977171 FV. 1 FV. 5 : 18051.774959

Gen: 99 | Ind: : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV. 4 : 10261.362468 FV. 5 : 18248.105848

Gen: 99 | Ind: 4

Gen: 99 | Ind: 11

FV. 1 : 1.8432e-6 FV. 2 : 7.8277e-7 FV. 3 : 3420.160779 Gen: 99 | Ind: FV. 4 : 11194.183761 1.9615e-6 FV. 51 : 18596.835547 FV. 2 : 7.7955e-7 FV. 3 : 4263.600798 FV. 4 : 9970.977171 FV. 5 : 18051.774959

FV. 2 : 7.8277e-7 FV. 3 : 3420.160779 FV. 4 : 11194.183761 FV. 5 : 18596.835547

Poor ranking in fc 1 and 3. 4

Gen: 99 | Ind: 19 FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 3 : 3533.411226 FV. 4 : 11319.216266 FV. 5 : 18019.240877

Gen: 99 | Ind: 12 FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV. 4 : 10261.362468 FV. 5 : 18248.105848

FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 FV. 4 : 10475.51295 FV. 5 : 17884.547923

Gen: 99 | Ind: 19 FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 3 : 3533.411226 FV. 4 : 11319.216266 FV. 5 : 18019.240877

Gen: 99 | Ind: 12 FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 Gen: 99 | Ind: FV. 3 : 3683.042066 FV. 1 : 1.8342e-6 FV. 4 : 10261.362468 FV. 2 : 7.8282e-7 FV. 5 : 18248.105848 FV. 3 : 3305.168806 FV. 4 : 10778.281182 FV. 5 : 18507.058042

Poor ranking in fc 4 and 5.

FV. FV.12: :1.9162e-6 7.8282e-7 FV. FV.23: :7.7664e-7 3305.168806 FV. FV.34: :4491.32147 10778.281182 FV. 5 : 18507.058042 Gen: 99FV. | Ind: 4 : 13 10005.035926 FV. 1 : 1.9396e-6 FV. 5 : 17847.172774 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 FV. 4 : 10475.51295 FV. 5 : 17884.547923

Gen: 99 | Ind: 13

Gen: 99 | Ind: 14

FV. 1 : 1.9396e-6 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 FV. 4 : 10475.51295 FV.5 1: 17884.547923 : 1.8804e-6 FV. FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 FV. 4 : 10441.052915 FV. 5 : 17742.995853

FV. 1 : 1.9162e-6 FV. 2 : 7.7664e-7 FV. 3 : 4491.32147 FV. 4 : 10005.035926 FV. FV. 15 :: 1.8613e-6 17847.172774 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

Gen: 99 | with Ind: 6 Gen: 99 | Ind: 6 farely well overall, Gen: 99 | Ind: 7 Working only fc 5 with a poor ranking.

Gen: 99 | Ind: 8Gen: 99 | Ind: 8

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

Poor ranking in fc 1 andGen:3. Gen: 99 | Ind: 8 99 | Ind: 8 FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

Gen: 99 | Ind: 1 FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7 FV. 3 : 3934.001996 FV. 4 : 11522.452081 FV. 5 : 17848.236866

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

Gen: 99 | Ind: 1 FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7 FV. 3 : 3934.001996 FV. 4 : 11522.452081 FV. 5 : 17848.236866

Gen: 99 | Ind: 14

FV. 1 : 1.9162e-6 FV. 2 : 7.7664e-7 FV. 3 : 4491.32147 FV. 4 : 10005.035926 FV. 5 : 17847.172774

99 | 14 Ind: 13 Gen: 99 | Gen: Ind: FV. 1 : 1.9396e-6

FV. 1 : 1.9162e-6 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 FV. 2 : 7.7664e-7 Gen: 99 | Ind: 6 FV. 4 : 10475.51295 FV. 1 : 1.8804e-6 FV. 5 : 17884.547923 FV. 3 : 4491.32147 FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 FV. 4 : 10005.035926 FV. 4 : 10441.052915 FV. 5 : 17847.172774 FV. 5 : 17742.995853

FV. 1 : 1.8613e-6 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

99 | Ind: 7 Gen: 99 | Ind: 7 Gen: FV. 1 : 1.8613e-6

FV. 1 : 1.8804e-6 FV. 2 : 7.8317e-7 FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 FV. 3 : 4033.774968 FV. 4 : 10441.052915 FV. 4 : 10441.052915 FV. 5 : 17742.995853 FV. 5 : 17742.995853

Gen: 99 | Ind: 8

FV. 1 : 1.8613e-6 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

Gen: Gen: 99 | Ind: 6 99 | Ind: 6 FV. 1 : 1.8804e-6

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

FV. 1 : 1.8432e-6 Gen: 99 | Ind: 12 FV. 2 : 7.8277e-7

Gen: 99 | Ind: 7 Gen: 99 | Ind: 7

FV. 1 : 1.8804e-6 FV. 1 : 1.8804e-6 FV. 2 : 7.8317e-7 FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 FV. 3 : 4033.774968 FV. 4 : 10441.052915 FV. 4 : 10441.052915 FV. 5 : 17742.995853 FV. 5 : 17742.995853

Gen: 99 | Ind: 13

FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. 4 : 10914.866979 FV. 5 : 18037.302474

Gen: 99 | Ind: 5 Gen: 99 | Ind: 14 FV. 1 : 1.8342e-6

Gen: 99 | Ind: 6 99 | Ind: 6 Gen:

FV. 1 : 1.9396e-6 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 FV. 4 : 10475.51295 FV. FV.15: :1.8804e-6 17884.547923 FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 FV. 4 : 10441.052915 FV. 5 : 17742.995853

FV. 4 : 10555.452941 FV. 5 : 17991.902887

Gen: 99 | Ind: 18

Gen: 99 | Ind: 5

Gen: 99 | Ind: 4

FV. 1 : 1.9615e-6 2 : 7.7955e-7 3 : 4263.600798 99 | Ind: 13 4 : Gen: 9970.977171 1 : 1.9396e-6 5 : FV. 18051.774959

FV. 1 : 1.9396e-6 Gen: 99 | Ind: 18 FV. 1 : 1.869e-6 FV. FV. 2 : 7.8021e-7 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. FV. 3 : 4301.443093 FV. 4 : 10914.866979 FV. 5 : 18037.302474 FV. 4 : 10475.51295 FV. Gen: 99 | Ind: 11 FV. 1 : 1.8432e-6 FV. 5 : 17884.547923 FV.

Gen: 99 | Ind: 3

FV. 1 : 1.8342e-6 FV. 2 : 7.8282e-7 Gen: 99 | Ind: FV. 3 : 3305.168806 FV. FV. 1 4 :: 1.9162e-6 10778.281182 FV. 2 5 : 7.7664e-7 18507.058042 FV. 3 : 4491.32147 FV. 4 : 10005.035926 FV. 5 : 17847.172774

FV. 1 : 1.9396e-6 FV.42: :9970.977171 7.8021e-7 FV. FV. 2 : 7.8021e-7 FV.53: :18051.774959 4301.443093 FV. FV. 3 : 4301.443093 FV. 4 : 10475.51295 FV. 4 : 10475.51295 FV. 5 : 17884.547923 FV. 5 : 17884.547923

Gen: 99 | Ind: 13

FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 4 : 10261.362468 1 : 1.9541e-6 FV. 5 : 18248.105848 FV. 3 : FV. 3683.042066 2 : 7.7622e-7 FV. 4 : FV. 10261.362468 FV. 5 : FV. 18248.105848 3 : 4172.860084

Gen: 99 | Ind: 12

Gen: 99 | Ind: 11

FV. 1 : 1.9615e-6

FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. 4 : 10914.866979 FV. 5 : 18037.302474

FV. 1 : 1.9541e-6 FV. 2 : 7.7622e-7 FV. 3 : 4172.860084 FV. 4 : 10555.452941 FV. 5 : 17991.902887

Gen: | Ind: 4 FV. 1 : 1.9082e-6 FV. 3 : 99 3420.160779 FV. FV.1 4: 1.9615e-6 : 11194.183761 FV. 2 : 7.8902e-7 FV. FV.2 5: 7.7955e-7 : 18596.835547 FV. 3 : 4263.600798 12 FV. 3 : 3683.042066 FV. 4 : 9970.977171 FV. 5 : 18051.774959 FV. 4 : 10261.362468 FV. 5 : 18248.105848

Gen: 99 | Ind: 13 FV. 2 : 7.7955e-7 Gen: 99 | Ind: 13 FV.31: :4263.600798 1.9396e-6 FV.

Gen: 99 | Ind: 18

3 Gen: 99 | Ind: 3

Gen: 99 | Ind: 12

FV. 1 : 1.8432e-6 FV. 11 2 : 7.8277e-7 Gen: 99 | Ind: FV. 1 : 1.8432e-6 FV. 3 : 3420.160779 FV. 2 : 7.8277e-7 FV. 4 : 11194.183761 FV. 5 : 18596.835547 FV. 3 : 3420.160779 FV. 4 : 11194.183761 FV. 5 : 18596.835547

Gen: 99 | Ind: 2

FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 3 : 3533.411226 FV. 4 : 11319.216266 FV. 5 : 18019.240877

FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7 Gen: 99 | Ind: 19 FV. 3 : 3934.001996 99 | Ind: 16 FV. 3Gen: : 3934.001996 FV. 1 : 1.8713e-6 FV. 1 : 1.9252e-6 FV. 4 : 11522.452081 FV. 4FV.: 211522.452081 : 7.8228e-7 FV. 2 : 7.8506e-7 Gen: 99 9 Gen: 99 | Ind: 9 FV. 3 : 3784.52184 FV. 5| :Ind: 17848.236866 FV. 5FV.: 4117848.236866 FV. 1 : 1.8656e-6 1.8656e-6 FV. 3 : 3533.411226 : 10245.734877 FV. 2 : 7.836e-7 2 : 17964.637644 7.836e-7 FV. 5 FV. 4 : 11319.216266 FV. 3 : 3186.31059 FV. 3 : 3186.31059 Gen: 99 | Ind: 10 FV. 4 : 10792.269907 FV. 4 : 10792.269907 FV. 5 : 18019.240877 FV. 599 : 18673.159006 FV. 5 : 18673.159006 Gen: |FV. Ind:1 2: 1.8584e-6 Gen: 99 | Ind: 2 FV. 1 : 1.896e-6 FV. 1 : 1.896e-6 FV. 2 : 7.8365e-7 FV. 2 : 7.8753e-7 FV. 2 : 7.8753e-7 FV. 3 : 3360.856862 FV. 3 : 3056.264454 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 4 : 10759.431675 Gen: 99 |FV. Ind: 19 FV. 4 : 10646.820418 FV. 5 : 18286.02663 5 : 18286.02663 FV. 1 : 1.8713e-6 FV. 5 : 18296.133586 FV. 2 : 7.8506e-7 Gen: 99 | Ind: FV. 3 : 3533.411226 FV. 1 : 1.9541e-6 FV. 4 : 11319.216266 FV. 2 : 7.7622e-7 FV. 5 : 18019.240877 FV. 3 : 4172.860084 FV. 4 : 10555.452941 FV. 5 : 17991.902887

2 Gen: 99 | Ind: 2

FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 19

: 1.8713e-6 FV.FV. 1 :11.8584e-6 : 7.8506e-7 FV.FV. 2 :27.8365e-7 : 3533.411226 FV.FV. 3 :33360.856862 FV. 4 : 11319.216266 : 1.9397e-6 FV.FV. 4:1 10646.820418 : 18019.240877 FV. : 7.8098e-7 FV.FV. 5 :52 18296.133586

Gen: 99 | Ind: 1

Gen: 99 | Ind: 0

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262 Gen: 99 | Ind: 1 FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7 FV. 3 : 3934.001996 FV. 4 : 11522.452081 FV. 5 : 17848.236866

FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7 FV. 3 : 3934.001996 FV. 4 : 11522.45208 FV. 5 : 17848.23686

Gen:99 99| |Ind: Ind:10 19 Gen:

FV. 4 : 10792.269907 FV. 1 : 1.869e-6 FV. 53 :: 18673.159006 3273.610832 FV. FV. 2 : 7.9318e-7 FV. 4 : 10914.866979 FV. 3 : 3273.610832 FV. 5 : 18037.302474 FV. 4 : 10914.866979 FV. 1 : 1.9496e-6 FV. 5 : 18037.302474

FV. 2 : 7.8076e-7 FV. 3 : 4175.840713 FV. 4 : 9964.877256 FV. 5 : 17927.484914

Gen: 99 | Ind

FV. 2 : 7.8098e-7 FV. 3 : 3934.001996 FV. 4 : 11522.452081 FV. 5 : 17848.236866

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

Gen: 99 | Ind: 14

FV. 1 : 1.9162e-6 FV. 2 : 7.7664e-7 FV. 3 : 4491.32147 FV. 4 : 10005.035926 FV. 1 : 1.8613e-6 FV. 5 : 17847.172774 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

Gen: 99 | Ind: 7 Poor ranking in fc 2, 4 and 5. Gen: 99 | Ind: 0

FV. 1 : 1.9496e-6 FV. 2 : 7.8076e-7 FV. 3 : 4175.840713 FV. 4 : 9964.877256 FV. 5 : 17927.484914

Gen: 99 | Ind: 8

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

Gen: 99 | Ind: 8

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262 Gen: 99 | Ind: FV. 1 : 1.9397e-6

FV. 2 : 7.8098e-7 Gen: Ind:0 9 Gen: 99399 | |Ind: FV. : 3934.001996

FV. 4 : 11522.452081 : 1.8656e-6 FV.FV. 1 : 11.9496e-6 FV. 5 : 17848.236866 : 7.836e-7 FV.FV. 2 : 27.8076e-7 : 3186.31059 FV.FV. 3 : 34175.840713 : 10792.269907 FV.FV. 4 : 49964.877256 : 18673.159006 FV.FV. 5 : 517927.484914

Gen: 99 | Ind: 9 FV. 1 : 1.8656e-6 FV. 2 : 7.836e-7 FV. 3 : 3186.31059 FV. 4 : 10792.269907 FV. 5 : 18673.159006

Gen:99 99| |Ind: Ind:0 9 Gen: 99 | Ind: 9 Gen:

Gen: 99 | Ind: 8

FV.1 1: 1.9496e-6 : 1.8656e-6 FV. FV.2 2: 7.8076e-7 : 7.836e-7 FV. FV.3 3: 4175.840713 : 3186.31059 FV. FV. FV.4 4: 9964.877256 : 10792.269907 FV. FV.5 5: 17927.484914 : 18673.159006

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

Gen: 99 | Ind: 1

FV. 1 : 1.8613e-6 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

Gen: 99 | Ind: 9 FV. 1 : 1.8656e-6 FV. 2 : 7.836e-7 FV. 3 : 3186.31059 FV. 4 : 10792.269907 FV. 5 : 18673.159006

FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7 Gen: 99 | Ind: 2 Gen: 99 | Ind: 1 FV. 1 : 1.896e-6 FV. 3 : 3934.001996 FV. 2 : 7.8753e-7 FV. 1 : 1.9397e-6 FV. 4 : 11522.452081 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 2 : 7.8098e-7 FV. 5 : 17848.236866 FV. 5 : 18286.02663 FV. 3 : 3934.001996 FV. 4 : 11522.452081 FV. 5 : 17848.236866

Poor ranking in fc 2, 4 and 5. Gen: 99 | Ind: 9 FV. 1 : 1.8656e-6 FV. 2 : 7.836e-7 FV. 3 : 3186.31059 FV. 4 : 10792.269907 FV. 5 : 18673.159006

Gen: 99 | Ind: 2

FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

FV. 1 : 1.8656e-6 FV. 2 : 7.836e-7 FV. 3 : 3186.31059 FV. 4 : 10792.269907 FV. 5 : 18673.159006

Gen: 99 | Ind: 2 Gen: 99 | Ind: 3

FV. 1 : 1.9541e-6 FV. 2 : 7.7622e-7 FV. 3 : 4172.860084 FV. 4 : 10555.452941 FV. 5 : 17991.902887

FV. 1 : 1.896e-6 99 | Ind: 3 FV. 2 : Gen: 7.8753e-7 FV. 1 : 1.9541e-6 FV. 3 : FV. 3056.264454 2 : 7.7622e-7 3 : 4172.860084 FV. 4 : FV. 10759.431675 FV. 4 : 10555.452941 FV. 5 : FV. 18286.02663 5 : 17991.902887

Gen: 99 | Ind: 2

FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

FV. 5 : 18116.625625

FV. 5 : 18037.302474

FV. 5 : 18116.625625

As we scanned through the different solutions, we realised that we needed the best average ranked individual, which has Gen: 99 | Ind: 18 Gen: 99 | Ind: 17 the fittest rankings per fitness when working together. Hence, in the next step, we extracted individual 14 gen 91 which has the best average ranking Gen: 99 | Ind: 18 Gen: 99 |out Ind: 17of all solutions (mentioned on the next page). Gen: 99 | Ind: 15

Gen: 99 | Ind: 16

Gen: 99 | Ind: 15

FV. 1 : 1.8766e-6 FV. 2 : 7.849e-7 FV. 3 : 3605.589886 FV. 4 : 11186.817572 FV. 5 : 17990.597995

FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 4 : 10245.734877 FV. 5 : 17964.637644

FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 1 : 1.9005e-6 FV. 5 : 18116.625625 FV. 2 : 7.818e-7

FV. 1 : 1.8766e-6 FV. 2 : 7.849e-7 FV. 3 : 3605.589886 FV. 4 : 11186.817572 FV. 5 : 17990.597995

FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

Gen: 99 | Ind: 16 FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 4 : 10245.734877 FV. 5 : 17964.637644

Gen: 99 | Ind: 17

FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

FV. 1 : 1.9005e-6

2 : 7.818e-7 FV. 1 : 1.869e-6 FV. FV. 3 : 3862.988455 FV. 2 : 7.9318e-7 FV. 4 : 10096.553409 FV. 5 : 18116.625625 FV. 3 : 3273.610832 FV. 4 : 10914.866979 FV. 5 : 18037.302474

FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. 4 : 10914.866979 FV. 5 : 18037.302474

Gen: 99 | Ind: 10

Gen: 99 | Ind: 17

FV. 1 : 1.8584e-6 FV. 2 : 7.8365e-7 Gen: 99 |FV. Ind:317 : 3360.856862 FV. 1 : 1.9005e-6 FV. 4 : 10646.820418 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 5 : 18296.133586 FV. 4 : 10096.553409

Gen: 99 | Ind: 19

Gen: 99 | Ind: 10

FV. 1 : 1.8584e-6 FV. 2 : 7.8365e-7 FV. 3 : 3360.856862 FV. 4 : 10646.820418 FV. 5 : 18296.133586

Gen: 99 | Ind: 17 FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

FV. 1 : 1.8584e-6 FV. 2 : 7.8365e-7 FV. 3 : 3360.856862 FV. 4 : 10646.820418 FV. 5 : 18296.133586

FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 3 : 3533.411226 FV. 4 : 11319.216266 FV. 5 : 18019.240877

FV. 1 : 1.8584e-6 FV. 2 : 7.8365e-7 FV. 3 : 3360.856862 FV. 4 : 10646.820418 FV. 5 : 18296.133586

Gen: 99 | Ind: 12 FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV. 4 : 10261.362468 FV. 5 : 18248.105848

FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. 4 : 10914.866979 FV. 5 : 18037.302474

Gen: 99 | Ind: 11

FV. 1 : 1.8432e-6 FV. 2 : 7.8277e-7 FV. 3 : 3420.160779 FV. 4 : 11194.183761 FV. 5 : 18596.835547

Gen: 99 | Ind: 18 FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. 4 : 10914.866979 FV. 5 : 18037.302474

Gen: 99 | ranking Ind: 11 Poor in fc 4 and 5. FV. 1 : 1.8432e-6 FV. 2 : 7.8277e-7 FV. 3 : 3420.160779 FV. 4 : 11194.183761 FV. 5 : 18596.835547

FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 3 : 3533.411226

Gen: 99 | Ind: 5

FV. 1 : 1.8342e-6 FV. 2 : 7.8282e-7 FV. 3 : 3305.168806 FV. 4 : 10778.281182 FV. 5 : 18507.058042 Gen: 99 | Ind: 14

FV. 1 : 1.8342e-6 FV. 2 : 7.8282e-7 FV. 3 : 3305.168806 FV. 4 : 10778.281182 FV. 5 : 18507.058042

FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV. 4 : 10261.362468 FV. 5 : 18248.105848

Gen: 99 | Ind: 13 Poor ranking in fc 1 and 3. FV. 1 : 1.9396e-6 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093

FV. 4 : 10475.51295 Gen: 99 | Ind: 6 FV. 15 :: 1.8804e-6 17884.547923 FV. FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 FV. 4 : 10441.052915 FV. 5 : 17742.995853

Gen: 99 | Ind: 13

FV. 1 : 1.9396e-6 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 FV. 4 : 10475.51295 FV. 5 : 17884.547923

Gen: 99 | Ind: 14

FV. 1 : 1.9162e-6 FV. 2 : 7.7664e-7 FV. 3 : 4491.32147 Gen: 99 | Ind: FV. 4 : 10005.035926 FV. 1 : 1.8613e-6 FV. 5 : 17847.172774 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

Gen: 99 | Ind: 14

FV. 1 : 1.9162e-6 FV. 2 : 7.7664e-7 FV. 3 : 4491.32147 FV. 4 : 10005.035926 Gen: 99 | Ind: 17847.172774 FV. 15 : 1.8613e-6 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

FV. 1 : 1.8342e-6 FV. 2 : 7.8282e-7 FV. 3 : 3305.168806 FV. 4 : 10778.281182 FV. 5 : 18507.058042

FV. 1 : 1.9162e-6 Gen: 99 | Ind: FV. 2 : 7.7664e-7 FV.31: :4491.32147 1.8613e-6 FV. FV.42: :10005.035926 7.9567e-7 FV. FV.53: :17847.172774 3133.120874 FV. FV. 4 : 10989.167861 FV. 5 : 18163.916349

FV. 3 : 3605.589886 FV. 4 : 11186.817572 FV. 5 : 17990.597995

Gen: 99 | Ind: 7

Gen: 99 | Ind: 0

FV. 1 : 1.9496e-6 FV. 2 : 7.8076e-7 FV. 3 : 4175.840713 FV. 4 : 9964.877256 FV. 5 : 17927.484914

FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. |3 :Ind: 3784.52184 Gen: 99 7 FV. 1 : 1.8613e-6 FV. 4 : 10245.734877 FV. 2 : 7.9567e-7 Gen: 99 | FV. 5 : 17964.637644

FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7 FV. 3 : 3934.001996 FV. 4 : 11522.452081 FV. 5 : 17848.236866

Gen: 99 | Ind: 16

Ind: 7

FV. 3 : 3133.120874FV. 1 : 1.8613e-6 FV. 4 : 10989.167861 FV. 2 : 7.9567e-7 FV. 5 : 18163.916349 FV. 3 : 3133.120874

FV. 1Gen: : 1.8804e-6 99 | Ind: FV. 2FV.: 7.8317e-7 1 : 1.8766e-6 FV. 3FV.: 4033.774968 2 : 7.849e-7 3 : 3605.589886 FV. 4FV.: 10441.052915 4 : 11186.817572 FV. 5FV.: 17742.995853

Gen: 99 | Ind: 7

FV. 1 : 1.8613e-6 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 Gen: 99 | Ind: 8 FV. 5 : 18163.916349 FV. 1 : 1.9206e-6 Gen: 99 | Ind: 1

Gen: 99 | Ind: 16

FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 4 : 10245.734877 FV. 5 : 17964.637644

FV. 1 : 1.8766e-6 Gen: 99 | Ind: FV. 2 : 7.849e-7 FV. FV. 13 :: 1.9206e-6 3605.589886 FV. FV. 24 :: 7.7862e-7 11186.817572 FV. FV. 35 :: 4528.158705 17990.597995 FV. 4 : 10005.035926 FV. 5 : 17816.09262

Gen: 99 | Ind: 1 FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7 FV. 3 : 3934.001996 FV. 4 : 11522.452081 FV. 5 : 17848.236866

Gen: 99 | Ind: 0

Gen: 99 | Ind: FV. 1 : 1.9496e-6 FV. 1 : 1.869e-6 FV. 2 : 7.8076e-7 FV. 2 : 7.9318e-7 FV. 3 : 4175.840713 FV. 3 : 3273.610832 FV. 4 : 9964.877256 FV. 4 : 10914.866979 FV. 5 : 17927.484914

Gen: 99 | Ind: 2

FV. 4 : 10792.269907 FV. 5 : 18673.159006

Gen: 99 | Ind: 2

FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 17 FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 8

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

FV.FV. 1 :51.9496e-6 Gen: 99 | Ind: 3 : 18673.159006 FV. 1 : 1.9541e-6 FV.Gen: 2 : 7.8076e-7 99 | Ind: 18 FV. 2 : 7.7622e-7 FV. 3 : 4172.860084 FV.FV. 3 :14175.840713 : 1.869e-6 FV. 4 : 10555.452941 FV.FV. 4 :29964.877256 FV. 5 : 17991.902887 : 7.9318e-7 FV.FV. 5 :317927.484914 : 3273.610832

Gen: 99 | Ind: 17

FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 Gen: 99 | Ind: 10 FV. 5 : 18116.625625 FV. 1 : 1.8584e-6 Gen: | Ind: 3 FV. 2 : 99 7.8365e-7 FV. FV.1 3: 1.9541e-6 : 3360.856862 FV. FV.2 4: 7.7622e-7 : 10646.820418 FV. FV.3 5: 4172.860084 : 18296.133586 FV. 4 : 10555.452941 FV. 5 : 17991.902887

Gen: 99 | Ind: 18 FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. 4 : 10914.866979 FV. 5 : 18037.302474

FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

Gen: 99 | Ind: 18 FV. 1 : 1.869e-6

2 : 7.9318e-7 Gen: 99FV. 10 FV.| 3 : Ind: 3273.610832

FV.Gen: 4 : 10914.866979 99 | Ind: 11 FV. 1 : 1.8584e-6 FV.FV. 5 :118037.302474 : 1.8432e-6 FV. 2 : 7.8365e-7 FV. 2 :99 7.8277e-7 Gen: | Ind: 4 : 3420.160779 FV.FV. 1 :31.9615e-6 31.9397e-6 : 3360.856862 FV. FV. 1 : Gen: 99FV.FV. |2 :457.7955e-7 19 :Ind: 11194.183761 FV. : 18596.835547 : 10646.820418 FV. 3 : 4263.600798 FV. FV. 2 : 47.8098e-7 FV. 1 : 1.8713e-6 FV. 4 : 9970.977171 : 18296.133586 FV. 5 : 18051.774959 FV. FV. 3 : 53934.001996 FV. 2 : 7.8506e-7 FV. FV. 4 : 11522.452081 3 : 3533.411226 4 : 11319.216266 FV. FV. 5 : 17848.236866 FV. 5 : 18019.240877

Gen: 99 | Ind: 1

Gen: 99 | Ind: 4

FV. 1 : 1.9615e-6 FV. 2 : 7.7955e-7 FV. 3 : 4263.600798 FV. 4 : 9970.977171 FV. 5 : 18051.774959

Gen: 99 | Ind: 17

Results similar to that of generation 99, ind 16.

FV. 4 : 10914.866979 FV. 5 : 18037.302474

FV. 5 : 18037.302474

FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

Gen: 99 | Ind: 17

FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

FV. 1 : 1.8656e-6 FV. 2 : 7.836e-7 FV. 3 : 3186.31059 FV. 4 : 10792.269907 FV. 5 : 18673.159006

FV. 1 : 1.9005e-6 FV. 2 : 7.818e-7 FV. 3 : 3862.988455 FV. 4 : 10096.553409 FV. 5 : 18116.625625

Gen: 99 | Ind: 9

FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 4 : 10245.734877 FV. 5 : 17964.637644

Gen: 99 | Ind: 9

FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 FV. 3 : 4528.158705 FV. 4 : 10005.035926 FV. 5 : 17816.09262

FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 4 : 10245.734877 FV. 5 : 17964.637644

1 : 1.8656e-6 Gen: 99 | Ind: 16 Gen: 99 | Ind: 16 Has an overall average ranking and balances outFV. types of FV. all 2 : 7.836e-7 Gen: 99 | Ind: 9 FV. 3 : 3186.31059 network in the system. Gen: 99 | Ind: 0 FV. 4 : 10792.269907 FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 1 : 1.8656e-6 FV. 4 : 10245.734877 FV. 2 : 7.836e-7 FV. 5 : 17964.637644 FV. 3 : 3186.31059 Gen: | Ind: 2 FV. 4 : 99 10792.269907 FV. FV.1 5: 1.896e-6 : 18673.159006 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 8

Gen: 99 | Ind: 8 FV. 1 : 1.9206e-6 FV. 1 : 1.9206e-6 FV. 2 : 7.7862e-7 Gen: 99 | Ind: 16FV. 3 : 4528.158705 FV. 2 : 7.7862e-7 FV. 1 : 1.9252e-6 FV. 3 : 4528.158705 FV. 2 : 7.8228e-7 Gen: 99 | Ind: 9 FV. 4 : 10005.035926 FV. 3 : 3784.52184 FV. 1 : 1.8656e-6 FV. 4 : 10005.035926 FV. 5 : 17816.09262 FV.24: :7.836e-7 10245.734877 FV. FV. 5 : 17816.09262 FV.35: :3186.31059 17964.637644 FV.

Gen: 99 | Ind: 8

Gen: 99 | Ind: 16

FV. 1 : 1.9252e-6 FV. 2 : 7.8228e-7 FV. 3 : 3784.52184 FV. 4 : 10245.734877 FV. 5 : 17964.637644

FV. 5 : 17990.597995

Poor in fc 2 and 4. Gen: 99 | ranking Ind: 15

FV. 4 : 10989.167861 FV. 5 : 18163.916349

Gen: 99 | Ind: 16

FV. 1 : 1.8804e-6 FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 FV. 4 : 10441.052915 FV. 5 : 17742.995853

Gen: 99 | Ind: 6

FV. 1 : 1.8613e-6 FV. 2 : 7.9567e-7 FV. 3 : 3133.120874 FV. 4 : 10989.167861 FV. 5 : 18163.916349

FV. 1 : 1.8804e-6 FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 FV. 4 : 10441.052915 FV. 5 : 17742.995853

Gen: 99 | Ind: 6

FV. 2 : 7.7664e-7 FV. 3 : 4491.32147 FV. 4 : 10005.035926 FV. 5 : 17847.172774

Poor ranking in fc 1 and 3. Gen: 99 | Ind: 14

Gen: 99 | Ind:

FV. 1 : 1.8804e-6 FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 Gen: 99 | Ind: 15 FV. 4 : 10441.052915 FV. 1 : 1.8766e-6 FV. 5 : 17742.995853 FV. 2 : 7.849e-7

Gen: 99 | Ind: 5

Gen: 99 | Ind: 6

FV. 1 : 1.9162e-6 FV. 2 : 7.7664e-7 FV. 3 : 4491.32147 FV. 4 : 10005.035926 FV. 5 : 17847.172774

FV. 1 : 1.8342e-6 FV. 2 : 7.8282e-7 FV. 3 : 3305.168806 99 | Ind: FV. 4 Gen: : 10778.281182 FV. 5 FV. : 18507.058042 1 : 1.9162e-6

FV. 1 : 1.9396e-6 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 FV. 4 : 10475.51295 FV. 5 : 17884.547923

Gen: 99 | Ind: 13

FV. 1 : 1.9396e-6 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 FV. 4 : 10475.51295 FV.5 1: 17884.547923 : 1.8804e-6 FV. FV. 2 : 7.8317e-7 FV. 3 : 4033.774968 FV. 4 : 10441.052915 FV. 5 : 17742.995853

FV. 3 : 3305.168806 FV. 4 : 10778.281182 FV. 5 : 18507.058042

Gen: 99 | Ind: 12

FV. 1 : 1.9396e-6 FV. 2 : 7.8021e-7 FV. 3 : 4301.443093 FV. 4 : 10475.51295 FV. 5 : 17884.547923

99 | Ind: 13 99 | Ind: Poor ranking in fc 2 Gen: and 5,12 and have average ranksGen:for the Gen: 99 | Ind: 5 other fitnesses as well. Gen: 99 | Ind: 6 Gen: 99 | Ind: 5 FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV.14: :1.8342e-6 10261.362468 FV. FV.25: :7.8282e-7 18248.105848 FV.

Gen: 99 | Ind: 12

Gen: 99 | Ind: 13 Gen: 99 | Ind: 13

Gen: 99 | Ind: 12

FV. FV.15: :1.9082e-6 18019.240877 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV.41: :10261.362468 1.8342e-6 FV. FV.52: :18248.105848 7.8282e-7 FV. FV. 3 : 3305.168806 FV. 4 : 10778.281182 FV. 5 : 18507.058042

FV. 2 : 7.8506e-7 FV. 3 : 3533.411226 FV. 4 : 11319.216266 FV. 5 : 18019.240877

FV. 3 : 3533.411226 FV. 4 : 11319.216266 FV. 5 : 18019.240877

FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV. 4 : 10261.362468 FV. 5 : 18248.105848

FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV. 3 : 3683.042066 FV. 4 : 10261.362468 FV. 4 : 10261.362468 FV. 5 : 18248.105848 FV. 5 : 18248.105848

Gen: 99 | Ind: 19

FV. 1 : 1.8432e-6 FV. 2 : 7.8277e-7 FV. 3 : 3420.160779 FV. 4 : 11194.183761 Gen: 99 | Ind: 19 FV. : 18596.835547 FV. 5 1 : 1.8713e-6

FV. 1 : 1.8432e-6 FV. 2 : 7.8277e-7 FV. 3 : 3420.160779 Gen: 99 | Ind: 19 FV. 4 : 11194.183761 FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 5 : 18596.835547

Gen: 99 | Ind: 12

Gen: 99 | Ind: FV.12 1 : 1.9082e-6

Gen: 99 | Ind: 12 FV. 4 : 11319.216266

Gen: 99 | Ind: 11

Gen: 99 | Ind: 18

Gen: 99 | Ind: 19

Gen: 99 | ranking Ind: 10 Poor in fc 2 and Gen: 995. | Ind: 10 FV. 5 : 18116.625625

FV. 1 : 1.8432e-6 FV. 2 : 7.8277e-7 FV. 3 : 3420.160779 FV. 4 : 11194.183761 FV. 5 : 18596.835547

FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 3 : 3533.411226 FV. 4 : 11319.216266 FV. 5 : 18019.240877

Gen: 99 | Ind: 10

Gen: 99 | Ind

Gen: 99 | Ind: 11

FV. 1 : 1.8584e-6 FV. 2 : 7.8365e-7 FV. 3 : 3360.856862 FV. 4 : 10646.820418 FV. 5 : 18296.133586

Gen: 99 | Ind: 1

FV. 1 : 1.9397e-6 FV. 2 : 7.8098e-7

Gen: | Ind: 19 FV. 399 : 3934.001996 FV.FV. 1 : 41.8713e-6 : 11522.452081 FV. 2 : 7.8506e-7 FV. 5 : 17848.236866 FV. 3 : 3533.411226 FV. 4 : 11319.216266 FV. 5 : 18019.240877

Gen: 99 | Ind: 9

Gen: 99 | Ind: 18

Gen: 99 | Ind: 0

FV. 1 : 1.9496e-6 FV. 2 : 7.8076e-7 FV. 3 : 4175.840713 FV. 4 : 9964.877256 FV. 5 : 17927.484914

FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. 4 : 10914.866979 FV. 5 : 18037.302474

FV. 1 : 1.8656e-6 FV. 2 : 7.836e-7 FV. 3 : 3186.31059 FV. 4 : 10792.269907 FV. 5 : 18673.159006

FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 18 Gen: 99 |4. Ind: 18 99 | Poor ranking in fc 2 and Gen: FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 3 : 3273.610832 FV. 4 : 10914.866979 Gen: 99 | Ind: 11 FV. 5 : 18037.302474 FV. 1 : 1.8432e-6 FV. 2 : 99 7.8277e-7 Gen: | Ind: 4 FV.13: 1.9615e-6 : 3420.160779 FV. FV. : 11194.183761 FV. 24: 7.7955e-7 FV.3 5: 4263.600798 : 18596.835547 FV. FV. 4 : 9970.977171 FV. 5 : 18051.774959

Gen: 99 | Ind: 10

Gen: 99 | Ind: 0

FV. 1 : 1.9496e-6 Gen: 99 | Ind: 18 FV. 2 : 7.8076e-7 FV. 1 : 1.869e-6 FV. 3 : 4175.840713 99 | Ind: 2 FV. Gen: 2 : 7.9318e-7 FV. 4 : 9964.877256 1 : 1.896e-6 FV. FV. 3 : 3273.610832 FV. 5 FV. : 17927.484914 FV. 2 : 7.8753e-7 4 : 10914.866979 3 : 3056.264454 FV. FV. 5 : 18037.302474

Ind: 2

FV. 1 : 1.869e-6 FV. 2 : 7.9318e-7 FV. 1 : 1.896e-6 FV. 3 : 3273.610832 FV. 2 : 7.8753e-7 FV. 4 : 10914.866979 Gen: 99 | FV. Ind:311 FV. 5 : 18037.302474 : 3056.264454 FV. 1 : 1.8432e-6 FV. 2 : 7.8277e-7 FV. 4 : 10759.431675 FV. 3 : 3420.160779 FV. 5 : 18286.02663 FV. 4 : 11194.183761 FV. 5 : 18596.835547

Gen: 99 | Ind: 11

FV. 1 : Gen: 1.8432e-6 99 | Ind: FV. 2 : FV. 7.8277e-7 1 : 1.896e-6 FV. 3 : FV. 3420.160779 2 : 7.8753e-7 FV. 4 : FV. 11194.183761 3 : 3056.264454 FV. 5 : FV. 18596.835547 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 2 FV. 1 : 1.896e-6 FV. 2 : 7.8753e-7 FV. 3 : 3056.264454 FV. 4 : 10759.431675 FV. 5 : 18286.02663

Gen: 99 | Ind: 19

FV. 1 : 1.8584e-6 FV. 2 : 7.8365e-7 FV. 3 : 3360.856862 Gen: 99 | Ind: FV. 4 : 10646.820418 FV.51: :18296.133586 1.8713e-6 FV. FV. 2 : 7.8506e-7 FV. 3 : 3533.411226 FV. 1 : 1.9397e-6 FV. 4 : 11319.216266 FV. 2 : 7.8098e-7 FV. 5 : 18019.240877

FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 3 : 3533.411226 FV. 4 : 11319.216266 Gen: 99 | Ind: 12 FV. 5 : 18019.240877 FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV. 4 : 10261.362468 FV. 5 : 18248.105848

Gen: 99 | Ind: 11

FV. 1 : 1.8432e-6 FV. 2 : 7.8277e-7 FV. 3 : 3420.160779 FV. 4 : 11194.183761 FV. 5 : 18596.835547

Gen: 99 | Ind: 1

Gen: 99 | Ind: 19 Poor ranking in fc 2 and 4. FV. 1 : 1.8713e-6 FV. 2 : 7.8506e-7 FV. 3 : 3533.411226 FV. 4 : 11319.216266 Gen: 99 | Ind: 12 FV. 5 : 18019.240877 FV. 1 : 1.9082e-6 Gen: 99 | Ind: 5 FV. 2 :17.8902e-7 FV. : 1.8342e-6 FV. 3 :23683.042066 FV. : 7.8282e-7 FV. 4 :310261.362468 FV. : 3305.168806 FV.FV. 5 :418248.105848 : 10778.281182 FV. 5 : 18507.058042

FV. 3 : 3934.001996 FV. 4 : 11522.452081 FV. 5 : 17848.236866

Gen: 99 | Ind: 1

FV. 1 : 1.9397e-6 Gen: 99 | Ind: 1999 | Ind: FV. 2 : 7.8098e-7 Gen: FV.|31 Ind: 1.8713e-6 FV. :: 3934.001996 Gen: 99 3 FV. 1 : 1.9541e-6 FV. 42 :: 11522.452081 7.8506e-7 FV. 2 : 7.7622e-7 FV. FV. 1 : 1.9541e-6 FV. 3 : 4172.860084 FV. 53 :: 17848.236866 3533.411226 FV. 2 : 7.7622e-7 FV. FV. 4 : 10555.452941 FV. 4 : 11319.216266 FV. 3 : 4172.860084 FV. 5 : 17991.902887 FV. 5 : 18019.240877 FV. 4 : 10555.452941

FV. 5 : 17991.902887

Gen: 99 | Ind: 3

FV. 1 : 1.9541e-6 FV. 2 : 7.7622e-7 Gen: | Ind: 13 FV. 3 : 99 4172.860084 FV. 1 : 1.9396e-6 FV. 4: :7.8021e-7 10555.452941 FV. 2 FV. 3 : 4301.443093 FV. 5 : 17991.902887 FV. 4 : 10475.51295 FV. 5 : 17884.547923

Gen: 99 | Ind: 13

Gen: 99 Gen: | Ind: 126 99 | Ind: FV. 1 : 1.9396e-6 FV. 2 : 7.8021e-7

FV.FV. 3 :14301.443093 Gen:FV. 991 |: 1.9082e-6 Ind: 3:: 1.8804e-6 FV.FV. 4 :210475.51295 7.8317e-7

FV.FV. 5 :317884.547923 : 4033.774968 FV. 1 : 1.9541e-6 FV. 2 : 7.8902e-7 FV. 4 : 10441.052915 FV. 2 : 7.7622e-7 FV. 3 : 3683.042066 FV. 5 : 17742.995853 FV. 3 : 4172.860084 FV. 4 : 10261.362468 FV. 4 : 10555.452941 FV. 5 : 18248.105848 FV. 5 : 17991.902887

Gen: 99 | Ind: 12 FV. 1 : 1.9082e-6 FV. 2 : 7.8902e-7 FV. 3 : 3683.042066 FV. 4 : 10261.362468 FV. 5 : 18248.105848

Best Average Solution

By selecting the best average ranked individual for all fitness criterion, we could observe that network connections as per shortest required pathways and linkage with the higher grounds, are equally distributed amongst all body parts..

ISOMETRIC VIEW - GEN 91 IND 14

Overall PCP graph

Rank 1 - PCP for Best average in all fitness criterias

RANKING

Worst

Best

936/1999

647/1999 145/1999

Best

fc1

Best

fc2

Best

fc3

Extracting rankings which we find satisfactory and choose to go ahead with.

517/1999

393/1999

Best

fc4

fc5

FC1 - Maximise distance b/w pond centers

FC2 - Maximise distance b/w towers

FC3 - Minimise distance b/w ponds and central hub

FC4 - Minimise distance b/w ponds, road and platforms

FC5 - Minimise overall path length amongst all clusters

The graphs show that the system is going towards optimisation where, it tries to find better solutions with respect to each fitness criterion with every passing generation. Also, the system finds a state of equilibrium in optimisation from halfway ahead untill the last generations, but has more individuals with variation.

HYPOTHESIS FOR FUT

Our urban development is going to be divided into two phases. Phase one being the settlement of 167 towers among clusters, that can house 50.000 people. Phase 2 will be taken forward once there arises a need to house more people with the rise in population of the area in coming decades. In order to further this, the towers can be built one at a time within each cluster of 5 towers and an urban hub, on the empty podium left out (marked by red colour in the site plan).

TURE DEVELOPMENT

APPROXIMATED EXTENSION FOR 15000 PEOPLE

LEGEND : SPOT FOR FUTURE TOWER SPOT FOR AQUACULTURE PONDS IN THE LOW WATER VELOCITY ZONES EXISTING

1.4.h TOWER DEVELOPMENT AND OPTIMISATION STRATEGY : BODY PARTS

Branched lines for exoskeleton

Skin

Floor plates

Combination

The basic idea for the tower development was to generate an intercollated system of columns which start from the base, functioning solely to rear mussel production and also act as a coastal defense. As it grows upwards from the podium level, we wanted to integrate them as a branched system of columns that act both as an exoskeleton and a structural system, which can take the load of the floor plates of the entire tower. This tower is meant for only residential housing and hence does not have much variation in terms of individual floor heights or separate service areas. Therefore, the morphology of the system is kept minimal, with the generation of the floorplate being a function of changing point positions within a circular geometry.

GENE REGULATION

OFFSET CURVES

DIVIDE CURVE

EVALUATE CURVE IN ORDER TO FORM NEW POINTS

NEW FLOOR PLATE GENERATED EITHER FROM 1 OR 2 NEW POINTS

STRETCHING POINTS IN FLOOR PLATES

FITNESS CRITERIAS

FC1 - MINIMISE HEIGHT

FC2 - MAXIMISE FLOOR AREA

As we can see from the diagram, fitness criterion 1 and 3 are in conflict with each other, whereas fitness criterion 2 and 3 work hand in hand.

FC3 - MINIMISE RELATIVE DIFFERENCE RADIUS

Tower Fitness criterias

Moving points according to vector

FC1 - Minimise Height

Genes Non uniform Scale 2d

Scale 1d in ht.

FC2 - Maximise floor Area

FC3 - Minimise the relative difference of the radius

The diagram below shows how the 3 genes affect each fitness criterion.

PARETO FRONT EXTRACTION :

We extracted the pareto fronts to analyse all the best possible solutions with variations. Out of the 10 pareto individuals from the last generation. In order to populate our system with the towers, we chose 5 from these 10 individuals that were different from each other, and we seeked variation as our design objective. We also observed that the simulation reached a point where the phenotypes were similar as we assume certain genes kept repeating a certain value and one or more fitness objectives were picked more often. The above marked individuals with blue dotted lines are the ones selected in order to populate our current network.

SELECTED TOWERS AND CENTRAL HUB (PUBLIC EQUIPMENT)

Gen 49 I 1

Gen 49 I 2

Gen 49 I 3

Gen 49 I 6

Gen 49 I 7

KARAMBA ANALYSIS

Hub module

One of the main objective of the design of the exoskeleton is the structural performance. The main design strategy selection was to select a range of individuals from the GA, test its structural performance, and based on the results, select the most suitable ones to populate the urban network. However, due to time constraints we could only analyze the selected five. All the below shown structural analysis has been performed taking into consideration only the force acting on the mesh due to gravity.

GEN 49 I 1

GEN 49 I 2

Displacement

Utilization

Displacement

Utilization

GEN 49 I 3

GEN 49 I 6

GEN 49 I 7

Displacement

Utilization

Displacement

Utilization

Displacement

Utilization

GEN 49 I1 The generated mesh is not capable of distributing all the loads equally along the nodes to the ground level. As shown in the displacement analysis, some of the members are under stress as they are not connected well to their neighbours and shows that it may undergo high amounts of displacement at the pink areas. This behaviour can also be seen in the utilization chart, the same branches of the mesh are in high tension or compression along those nodes.

GEN 49 I2 The generated mesh performs better than the pereviously conducted analysis, as all the loads are capable of distributing equally along the nodes except for one leg near the base. As shown in the displacement analysis, there is least amount of deformation at most of the connections, except for one. This behaviour is also backed by the utilisation chart, which shows the tension happening at the same branch which shows deformation. Overall, this is proven to be a good candidate of choice.

GEN 49 I3 This generated mesh more or less performs very similar to the individual 2, generation 49.

GEN 49 I6 This generated mesh performs the best as it has nodes which are capable of distributing the stresses equally along all the connections till the ground. As shown in the displacement analysis, there is least amount of deformation at the topmost connections and no visible deformations at the bottom, which makes it the best contender to take forward.

GEN 49 I7 The generated mesh is very similar to the analysis results of ind 1, gen 49 and ind 2, gen 49. They are not capable of distributing all the loads equally along the nodes to the ground level. As shown in the displacement analysis, some of the members are under stress as they are not connected well to their neighbours and shows that it may undergo high amounts of displacement at the pink areas. This behaviour can also be seen in the utilization chart, the same branches of the mesh are in high tension or compression along those nodes.

SECTION II

DESIGN PROPOSAL

PLAN

TOTAL TOWERS GENERATED : 167 TOTAL NO. OF PONDS GENERATED : 19 RED SPOTS LEFT OUT PER CLUSTER OF SIX SURROUNDING TOWERS AS FUTURE TOWER DEVELOPMENT WHICH IN PHASE 1 - ACT ONLY AS COASTAL DEFENSE AND OPEN AREAS ON PODIUM LEVEL

PREDICTED 9.6 M ACCOUNTING SEA LEVEL RISE

SECTION

CURRENTLY B/W 5.7 TO 6.7 M

DURING A STORM SURGE

DURING HIGH TIDE Naturally growing seaweed/sea grass

CURRENTLY B/W 0.7 TO 1.5 M

Ponds cum sand beds with sea cucumbers

DURING LOW TIDE

Oysters

Sea Cucumber

Deck Area Around Pond

Sea grass

Sea

Ramp connecting podium and pond deck

Podium of the tower building/refuge level grass

Mussels on the columns

Floating Sea weed agriculture Pods Mussel posts

Footing similar to Amphibious flood prone stilted structure.

Note: Site specific design as per structural engineer’s recommendation

PERSPECTIVE VIEW

CONCLUSION Looking back at our hypothesis, we find that our system has been able to achieve an integrated IMTA system that functions hand in hand with controlling and managing the water velocity and creating a habitable space for both humans and the ecosystem that had been lost. We found that starting an oyster culture at a local scale could be beneficial in terms of the nutritional self sufficiency at a seasonal scale and at the same time could also address the urban issues of an estuarine ecosystem. The experiments conducting by us in order to gain a thorough workflow for the whole system was taken forward in a linear fashion, which successfully drove our hypothesis proving various assumptions at different junctions. Yet, some questions remained unanswered due to the lack of time for further analysis. For example, although it was proven that the introduction of coastal defenses were capable of reducing the water velocity and successful in generating areas that could be suitable enough for oyster culture. However, the sediment accretion happening in and around these newly placed elements could not be evaluated as per the reduction in the flow of water or vice versa. Another area that we were interested in researching was the successful population of the naturally inhabiting species in and around the coastal shoreline which would invade once we stop growing any further sea wall and let the whole area flood, the behaviour of which we find unpredictable. Due to its unpredictability, we could not prove or simulate a similar environment in order to see whether the tidal terraces built in and around the IMTA ponds would be naturally taken over by saltmarshes or it would behave any differently due to variation in salinity and sedimentation happening due to the coastal defenses present in the vicinity. In terms of human habitat creation, we were capable of introducing variation in terms of architectural criterias such as maximum floor plate utilisation, slenderness of the tower and height. We were also able to create a seamless exoskeleton which started at the water level and functioned as mussel posts and as it grew further above the podium, successfully acted as a structural system able to transfer loads evenly top to bottom. Yet, due to time constraints, the tower lacked design input based on environmental criterias such as wind flow in and around the hoo peninsula, which could probably be helpful in moulding the shape of the tower aerodynamically; so on and so forth. We could have also taken into consideration the direction of the sun as an input to rationalise shading system that could be introduced on the facade of the towers; that would adapt to each one based on their locations.

BIBLIOGRAPHY [1.0] : Vashchenko, T. 2018. BETWEEN CITY AND SEA: multi-trophic mariculture as urban intertidal catalyst. Master of Landscape Architecture, thesis. University of Washington. Washington D.C. [2.0] : Corrazza, M et al. 2014. Collective Morphogenesis, Master of Architecture - Emergent Technologies and Design, thesis. Architectural Association, London, UK.

ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMMES COVERSHEET FOR SUBMISSION 2019-20

PROGRAMME:

Emergent Technology and Design

STUDENT NAME(S): Debolina Ray Maria Luiza Gomes Torres Maximo Tettamanzi

SUBMISSION TITLE

Design II Booklet

COURSE TITLE

Design II

COURSE TUTOR

Abhinav Chaudhary

DECLARATION:

Elif Erdine Alican Sungur Eleana Polychronaki

“I certify that this piece of work is entirely my/our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged.” Signature of Student(s):

Date: 16/03/2020