Design Experimentations (Bottom up approach) - 19-20
Cellular Automaton - 21-22
Conway Game of Life - 23-24
Form Finding - 25-28
Evolutionary Solver - 29-34
CGL alternative rules - 35-36
Eroded Taxonomy - 37-46
Contiguity Testing - 47-48
3D Prints - 49-50
Outlook - 51-56
Conclusion - 57-58
Bibliography - 59-61
Acknowledgment - 62
Keywords:
Emergent Design, Evolutionary Optimization, Cellular Automata, Conway Game of Life, Artiļ¬cial Intelligence.
Thesis Statement
Nature has perpetually wielded its inļ¬uence, adapting, mesmerizing, healing, inspiring, and inciting curiosity within the human soul, body, and mind. Traditionally, within our architectural profession, design has leaned towards pragmatic solutions over experimentation, favoring conventional approaches. However, I advocate for exploring novel solutions and methodologies by engaging with nature, drawing inspiration and insights from its inherent wisdom.
This thesis aims to delve into the natural formations found in various unique geological sites, with a particular focus on researching Erosional Formations and their correlation with the forces responsible for shaping them. The objective is to meticulously examine the sculpting techniques intrinsic to nature and integrate them into design practices. We will explore erosion as a form of craftsmanship, akin to an artist's hand guided by the elementsābe it wind, water, or iceāsculpting the landscape over million of years.
There is a profound richness in the spatial qualities and complexity engendered by emergent processes, often defying precise deļ¬nition and challenging comprehension, where the concept of randomness plays a signiļ¬cant role but is governed by concealed agents and principles.
Reimagining Erosion involves embracing the notion of subtraction as a tool for carving and form-ļ¬nding, uncovering the potential inherent in such processes as there are many lessons to be learnt from nature and adopted into our practices.
Thesis Breakdown
As the research started, Erosion became the sole focus of the research through design experimentations looking at the spatial outcome of such processes. Multiple different paths of experimentations were followed to understand the process of erosion and to explore the spatial geometrical outcome of such processes.
Initially, we delved into simulating erosion using Houdini VFX and adopted a top-down approach. Later, we transitioned to a bottom-up methodology, incorporating Cellular Automaton principles such as Conway's Game of Life while using evolutionary solver for control and to optimize for erodability pushing the forms to the brick of collapse.
Part A (Top-down approach)
Analysis & design experimentation on the macro scale
Analyzing erosion
ā¢ AI experimentations
ā¢ Simulating erosion
Physical experimentations
Part B (Bottom-up approach)
Analysis & design experimentation on the micro scale
ā¢ Cellular Automata
Conway Game of Life
ā¢ Evolutionary Solver
TOP DOWN APPROACH
SYNTHETICDATASETS
REFERENCES
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PART A
Top down approach Research design experimentations
ā¢ How does erosion happens ?
ā¢ Is it possible to simulate Erosion?
ā¢ What elements take part in the erosion process ?
Erosion
āis the geological process in which earthen materials are worn away and transported by natural forces such as wind or water. A similar process, weathering, breaks down or dissolves rock, but does not involve movementā
Erosion undergoes a complex process to be following:
Detachment
Detachment is the ļ¬rst stage of erosion where the material on the Earth's surface is loosened and removed. This can happen through various mechanisms:
ā¢ Hydraulic Action: This occurs when the force of ļ¬owing water dislodges and removes particles from the ground.
ā¢ Abrasion: Abrasion happens when particles carried by water, wind, or ice scrape against the Earth's surface, wearing it away.
ā¢ Chemical Weathering: Chemical processes such as oxidation, dissolution, or hydrolysis can weaken and dissolve rocks, making them more susceptible to erosion.
ā¢ Biological Activity: Plant roots can penetrate cracks in rocks, expanding them and causing detachment. Burrowing animals can also loosen soil and rock particles.
Transport
Once detached, the eroded material is transported away from its original location. The mode of transport depends on the agent of erosion:
ā¢ Water: Rivers, streams, and ocean currents can carry sediment for long distances. The speed and volume of water determine how much sediment can be transported and how far.
ā¢ Wind: Wind can pick up and transport small particles like sand and dust, especially in arid environments where vegetation cover is minimal.
ā¢ Glaciers: Glaciers can transport vast amounts of material as they move downslope, scraping and plucking rocks along the way.
Deposition
Deposition occurs when the transported material settles out of the transporting medium and comes to rest. This typically happens when the transporting agent loses energy or when the material encounters an obstacle:
ā¢ Water: Sediment carried by water settles when the ļ¬ow velocity decreases, such as in areas of reduced slope, where rivers enter lakes or oceans, or where vegetation slows the ļ¬ow.
ā¢ Wind: Wind-blown sediment settles when wind speed decreases, often behind obstacles like dunes, vegetation, or changes in terrain.
ā¢ Glaciers: When glaciers melt or lose their ability to carry sediment, they deposit their load, forming features like moraines, eskers, and outwash plains.
Throughout these processes, erosion shapes the Earth's surface by wearing down high points and depositing sediment in low-lying areas. This continuous cycle of detachment, transport, and deposition is a fundamental driver of landscape evolution.
Erosion Types
Physical Erosion
Physical erosion describes the process of rocks changing their physical properties without changing their basic chemical composition. Physical erosion often causes rocks to get smoother.
Chemical Erosion
Chemical erosion describes the process of rocks changing their chemical composition as they erode. The process that leads to the chemical erosion is called carbonation.
Erosion
Ice, usually in the form of glaciers, can erode the earth and create dramatic landforms. In frigid areas and on some mountaintops, glaciers move slowly downhill and across the land. Rocks carried by glaciers scrape the ground below, eroding both the ground and the rocks.
Erosion by Sind
Erosion by Water
Liquid water is the major agent of erosion on Earth. Rain, rivers, ļ¬oods, lakes, and the ocean carry away bits of soil and sand and slowly wash away the sediment.
Wind is a powerful agent of erosion. Aeolian (wind-driven) processes constantly transport dust, sand, and ash from one place to another.
Focusing the erosion research on Antelope Canyon was a deliberate choice, considering the wide variety of erosion types and causes found within. Selecting these canyons was intentional, as they offer semi-enclosed spaces that are helpful to the study's objectives.
Locating references and resources to understand the erosion process proved challenging and limited. Erosion is deemed an emergent phenomenon,
shaped over millions of years to form landscapes. Its continuous evolution over time renders it a dynamic force perpetually altering the terrain and making it more complicated to study and analyze. As the research for this thesis progressed, we analyzed erosion as a space-making technique.
To kickstart the design explorations, experimentations with Midjourney's text-to-image prompts became essential to visualize and contextualize the ideas. These exercises helped envision a realm where man-made structures seamlessly intertwined with natural landscapes, existing in perfect harmony. The resulting images portrayed a world where human creations coexisted alongside nature, preserving its serenity without causing any disruption.
The variation of images was studying
different scale of man-built structures that can adpat towards and within different geological sites and conditions such as sand dunes, canyons or sea shore hills.
Conclusion & Reļ¬ection
While the images produced are intriguing and demonstrate the power of Midjourney as a tool, transitioning these images into three-dimensional forms proved impossible. This limitation conļ¬nes Midjourney to being a visualization and imagination tool rather than a design tool
/Imagine āTall buildingās courtyard affected by erosion, inspired by the antelope canyon, ofļ¬ces over looking the courtyard, populated with many people, HD, cinematic realistic photoā
Simulating Erosion
In the initial quest to grasp erosion processes, we sought digital simulation methods, given the impracticality of waiting millions of years for natural erosion to happen. Following the research into viable approaches, we opted to utilize Houdini VFX to advance the study.
Houdini VFX
Houdini, developed by SideFX, stands as a visual effects (VFX) software renowned for its node-based procedural approach. This unique methodology enables exceptionally adaptable workļ¬ows across VFX, animation, and simulation tasks. Houdini excels in simulating erosion effortlessly.
Starting with a plane, users can transform it into a height ļ¬eld map using brush strokes. Once the height ļ¬eld map is established, Houdini facilitates erosion simulation, considering myriad factors pivotal to erosion dynamics. These factors encompass material properties, water ļ¬ow, volume of water, air movement, air direction, erodability, riverbank characteristics, and debris presence.
Achieving high-quality results in Houdini necessitates multiple iterations of erosion simulation to mirror the iterative process observed in nature. This iterative approach is paramount in attaining the intricate geometric outcomes that emerge from natural erosion processes.
Houdini Erosion Parameters
Hydro Erosion
Removal Rate
Proportion of Debris from hydro erosion that is deleted. This simulates water carrying away a certain amounts of debris rather than it all accumulating.
Max Debris Depth
Stop the erosion when the debris layer reaches (X) depth.
Grid Bias
Controls how biased the movement of material is in which direction.
Erodability Adjustments
Ramp-up Iterations
The iteration where erodability would get to its maximum value
Initial Factor
Controls how much of the set erodability would the material have on its initial movement
Slope Factor
Control how much the slope of the terrain would affect the erosion amount
Riverbed
Erosion Rate Factor
A multiplier on Erosion Rate that would cause a change on the riverbed only
Deposition Rate
Controls the rate at which the access sediment turns into debris
Sediment Capacity
The amount of sediment that can be carried per unit of moving water
Riverbank
Erosion Rate Factor
A multiplier on Erosion Rate that would cause a change on the riverbank only
Max Bank to Bed Water Ratio
The maximum of bank to bed water column height ratio that will be considered as river bank
Thermal Erosion
Removal Rate
Proportion of debris from thermal erosion that is deleted
Max Debris Depth
Stop erosion when the debris layer reaches this depth
Grid Bias
Controls how biased the movement of material is
Preciption
Amount
The amount of rainfall simulated in each frame
Density
How closely packed rain drops are
Evaporation Rate
The rate of evaporation
Raindrop Settings
Expand Radius
The affect of a single raindrop water to cover more cells on impact
Blur Radius
Rain blur radius
Water Flow
Quantization
The quantity of water/rain
Debris Flow
Spread Iterations
The number of iterations of water spreading simulation to do
Quantization
Controls the chunkiness of the debris ļ¬ow
Water Absorption
Controls the movement of the debris based on last frame ļ¬ow ļ¬eld
Max Height
The maximum height of wet debris that would not move due to water absorption
Repose Angle
The maximum slope at which loose solid material will remain in place without sliding
Global Seed
Random seed to make erosion vary for the same set of variables on the same input
The following diagram illustrates the steps taken to achieve a high-resolution design of an eroded landscape.
Simulating Erosion
This virtual eroded landscape stands as a testament to the rich array of spatial intricacies, diversity, and complexity inherent in eroded terrains. The diagram below shows the major erosion simulations the landscape underwent to achieve the high-resolution outcome.
Simulating Erosion
Research Design Experiment 03
After successfully simulating erosion effects using Houdini VFX, the conversation turned towards applying erosion principles to architectural geometric shapes. The design experiment showcased here centered on eroding a basic superrectangle geometry to explore the feasibility of erosion techniques in Houdini.
The simulation process involves dividing the simple geometry into smaller segments along the U and V directions, which will subsequently be unrolled and ļ¬attened.
Once the surface is ļ¬attened, it will be treated as a heightļ¬eld map in order to effectively simulate erosion.
Conclusion
While Houdini excels in erosion simulation, it currently relies on only a height ļ¬eld map approach. This means that it treat any shape as a landscape to manipulate further, posing a limitation on direct simulation without this mapping method.
Reļ¬ection
After facing the limitations with Houdini VFX. The approach towards the research, design and experimentations shifted towards a bottom up approach.
Original superrectangle shaped geometry Divided into smaller segments in UV direction
Unrolled surface
Landscape to project
Final eroded geometry
PART B
Bottom up approach Research design experimentations
ā¢ Can we analyze erosion on the micro scale ?
ā¢ What processes would we to follow ?
ā¢ What lessons can we learn from the erosion process?
Cellular Automaton
Studying the micro scale of erosion
Cellular Automata (CA) are computational models used to simulate complex systems consisting of simple components called cells. Each cell can exist in a ļ¬nite number of states and interacts with its neighboring cells based on a set of predeļ¬ned rules. These interactions occur in discrete time steps, with each step representing a new generation of the system.
The behavior of a cellular automaton emerges from the collective actions of its individual cells following the speciļ¬ed rules. While the rules are typically deterministic and local (i.e., based only on the cell's own state and the states of its neighbors), the overall behavior of the system can exhibit patterns and dynamics that are complex and unpredictable.
They are particularly useful for modeling phenomena where complex
behavior arises from simple rules and interactions, such as pattern formation, self-organization, and emergent properties.
As a basic demonstration of rule iteration, the example below illustrates Rule 30 and the resulting behavioral patterns arising from the initial seed. As the rules alter, the complexity and composition of the patterns also changes.
Cellular automata emerged as a fundamental component in this research, enabling the examination and analysis of erosion at a microscopic cellular level that led to the exploration of Conway game of life afterwards.
Rule 30 (Seeds)
Rule 30 Pattern Outcome
RULES (SEEDS)
The below examples showcase the relation between the different rules of CA (Seeds) in relation to the outcome patterns.
Conway Game of Life
Studying the micro scale of erosion
The Conway's Game of Life is a cellular automaton devised by mathematician John Conway in 1970. The game consists of a grid of cells, each of which can be in one of two states: alive or dead. The grid evolves through discrete time steps according to a set of rules as follow
Living cell with less than two live neighbors dies (Underpopulation)
Living cell with more than three live neighbors dies (Overcrowding)
Living cell with two to three live neighbors survives (Survival)
Dead cell with exactly three live neighbors become alive (Birth)
The beauty of CGL lies in its dynamic behavioral geometry, which continuously evolves and changes based on the states of its neighboring cells.
(Underpopulation)
(Overcrowding)
These rules determine the next state of each cell based on its current state and the states of its eight neighbors. The initial pattern constitutes the 'seed' of the system. From there, the evolution of the grid is determined solely by the initial state, with no further input from the user.
Game of Life is not played by individuals like a traditional game; instead, it's more of a simulation demonstrating how simple rules can lead to complex and unpredictable behavior
Following the standard rule of Conway Game of Life, you can realize the different typologies that exist within them as indicated on the right side.
Still Lifes - Never changes
Oscillators - Loops through different generations to the initial state
Spaceships - Loops through different generations to the initial state while moving
Living Cells
Dead Cells
Block Boat
Tub Loaf
Bee-hive
Pulser
Penta-decathoon
Beacon Toad
Blinker
Form Finding Through Conway Game
of Life
The initial concept involved reimagining and employing CGL in a three-dimensional context, wherein each evolution of Conway's Game of Life unfolds vertically upon the preceding evolution. This process serves as a method of form discov ery, where living cells are continu ously depicted by visible blocks while dead cells are eliminated
Through the intricate dynamics of CGL, we begin to generate forms that start to emerge into distinct typologies depending on the initial
The diagram below illustrates the design exploration strategy in action. On the right side, you observe CGL represented in a 2D dimension, while on the left side, the evolution of the form unfolds and emerge as CGL continues to progress.
Evolution #10
Evolution #20
Evolution #30 Evolution #40 Evolution #50
Form Finding
Designing with CGL
In order to materialize the initial concept, activation points (seeds) were strategically placed on an (11 x 11) grid, chosen manually to initiate an understanding of cell interactions with their neighbors and the emergence of the overall form.
The initial tests yielded three distinct forms, serving as a starting point to comprehend the creation of these intricately complex structures. Despite their seemingly random appearance, these forms are designed through a straightforward system, continuously evolving based on the relationships between the seeds and their neighbors.
Manual selection of seeds using Cellular Automata (CGL) proved highly inefļ¬cient, as predicting the ļ¬nal form became nearly impossible. As a reuslt it became imperative to explore more effective methods for controlling CGL.
Evolutionary Solver
Galapagos solver in grasshopper
As manual selection and input of points led to restricted outcomes, the adoption of an evolutionary solver became essential to address the complexities inherent in Conway's Game of Life, aiming for improved ļ¬tness and maximizing the number of cubes, thus shaping the evolving form.
Galapagos employs genetic algorithms, drawing inspiration from natural selection and evolution, to tackle optimization challenges. These algorithms iterate through generating a population of potential solutions, evaluating their ļ¬tness, and selecting the most promising ones to breed offspring for the next iteration.
Across successive generations, these solutions evolve and reļ¬ne until an optimal or satisfactory result is achieved.
To enhance results, it's crucial to grasp the core essentials of evolutionary solvers as follow :
ā¢ āEvolutionary Algorithms areĀ slow. Dead slow. It is not unheard of that a single process may run for days or even weeksā
ā¢ āEvolutionary AlgorithmsĀ do not guarantee a solutionā
ā¢ āEvolutionary Algorithms have a progressive run-time processā
ā¢ āEvolutionary Solvers spew forth a never ending stream of answers, where newer answers are generally of a higher quality than older answers.ā
ā¢ āEvolutionary Solvers allow -in principle- for a high degree ofĀ interactionĀ with the user.ā
Solving for better ļ¬tness
The ļ¬tness criteria initially aimed at maximizing the quantity of blocks generated through Conway's Game of Life. As testing commenced with Conway's Game of Life, it became an additive process to assess its suitability and the effectiveness of the solver in optimizing the results.
1 David Rutten, March 4. 2011. Evoltionary Principles applied to Problem Solving.https://ieatbugsforbreakfast.wordpress.com/2011/03/04/epatps01/
Initial Gene Selection
Random Selection
Selection by genomic distance
Breeding
Generation Zero
Coupling
( In-Breeding vs
out-breeding )
Best Performing Genes
Worst Performing Genes
Selection by genomic distance
Breeding
Generation One
Best Performing Genes
Coupling
( In-Breeding vs
out-breeding )
Worst Performing Genes
Reading the Graphs
Designing through data analysis
When utilizing Galapagos as an evolutionary solver, the process of mating and pairing to identify superior performing genes is depicted through a graph illustrating the optimization's efļ¬ciency. As the multi-dimentional-point-graph readings improve, our comprehension of how to manipulate it for better results deepens.
Continuing research and testing gradually shifts focus from analyzing the forms to interpreting the graphs, as the ultimate aim is to achieve the ļ¬ttest possible outcome through optimization.
Various scenarios arise when continuously analyzing the data while solving Conway's Game of Life, as follows:
Case 01
Continuously cycling without achieving a more optimal outcome.
Case 02
Exponentially discovering ļ¬tter outcomes, yet encountering a loop at the end, mistakenly believing it has found the optimal solution.
Case 03
Expenentionally ļ¬nding ļ¬tter results the longer the optimizing is running
Case 04
Continuously cycling without achieving a more optimal outcome.
Case 05
Looping for generations but suddenly ļ¬nding ļ¬tter results exponentially
Evolutionary Solver Interface
Case 01
Case
Case
Case
Optimizing the Optimization
Galapagos solver in grasshopper
Initially, solving CGL through evolutionary solver was only able to yield ļ¬tness rates of 10 to 15%. But after optimizing and understanding the solver thoroughly we were able to optimize the ļ¬tness for better results reaching up to 26%. This means that 26% of the entire form was solidly created simply by optimizing the seed selection with Galapagos.
The ļ¬tness criteria was only able achieve a higher rate by adjusting the following settings:
Initial Boost
Population multiplication factor for the ļ¬rst generation.
Increasing the initial boost helped reach ļ¬tter results in earlier stages of the simulation
Inbreed
Inbreeding factor (-100% is fully zoo-philic, +100% is fully incestuous).
Increasing the percentage of inbreeding accelerated the selection of genes, leading to quicker attainment of ļ¬tter results, as the closely related breeding appeared to yield superior outcomes
Cooling
The factor to lower the temperature with at each jump to another combination of parameter values
Reducing the temperature of each jump aided the simulation in selecting closer adjacent neighbors, resulting in faster attainment of ļ¬tter outcomes for solving CGL
The following forms resulted from the second batch of experiments, guided primarily by the evolutionary solver.
CGL - Discovering new rules
After extensive testing with Conway's Game of Life, we determined that the initial B3 S23 rule was insufļ¬cient in achieving our desired outcome. This prompted us to explore alternative rules within CGL. The standard rule yielded only a 26% ļ¬tness rate even after the settings optimization. Our aim was to enhance the evolutionary optimization to achieve a ļ¬tness rate of 50% or higher
In our exploration of different CGL rules, we began by considering the total number of existing rules. Each cell in CGL has 8 neighbors, resulting in 65,025 possible outcomes between the birth and survival rules. Recognizing the impracticality of testing each one individually, we identiļ¬ed a library containing the top 100 lifelike rules, which we further reļ¬ned to the top 12 most lifelike rules.
It's noteworthy that while our initial CGL tests followed an additive process, upon discovering lifelike rules, we transitioned to using CGL as a subtractive tool within the overall forms, echoing the concept of erodability and subtraction. Total
1 2 3 4 5 6 7 8
Top 12 lifelike rules
Eroded Taxonomy
Conway game of life death
After ļ¬ltering the most lifelike rules, we initiated the creation of a Taxonomy on a simple grid measuring (14x14) with 75 evolutions of CGL. This served to anchor the testing outcomes and facilitate data comparison. As Conway's Game of Life generated highly organic and intricate shapes, exploring the initial state of the seeds that inļ¬uenced the entire form was particularly fascinating.
The drawings below depict the initial seed for each form, illustrating how the entirety of the form was shaped by the initial state and the use of evolutionary optimization to solve it.
The ļ¬tness rate for these speciļ¬c rules varied from 64%+ to as low as 16%. The Taxonomy is arranged in order of ļ¬ttest to lamest from top to bottom and left to right. While some rules achieved better results than others due to their evolving behavior, what is notable is that they all developed their own typologies. The taxonomy presented here showcases the dead cells remaining once the form is carved away, revealing the deadcells of Conway's Game of Life.
Eroded Taxonomy
DAY & NIGHT 01
B3678 S34678
Conway game of life death | organized by ļ¬ttest to lames 1 2
Fitness Rate 63%
Initial Points 41
B25 S234578
Fitness Rate 59%
Initial Points 28
LANDRUSH
CORAL 01
B3 S45678
Fitness Rate 58%
DAY & NIGHT 02
Initial Points 41 3 4
Initial Points 30
B3678 S34678
Fitness Rate 57%
Eroded Taxonomy
Conway game of life death | organized by ļ¬ttest to lames
REPLICATOR
B1357 S1357
Fitness Rate 52%
Initial Points 28 5 6
MAZE
B3 S12345
Fitness Rate 50%
Initial Points 10
NEVER HAPPY
B345 S0456
GEOLOGY 01
B3578 S24678
Initial Points 30 7 8
Fitness Rate 48%
Initial Points 34
Eroded Taxonomy
Conway game of life death | organized by ļ¬ttest to lames
CORAL 02 B3 S45678
Fitness Rate 48%
Initial Points 30 9 10
3_4 LIFE B34 S34
Fitness Rate 43%
Initial Points 40
AMOEBA
B357 S1358
Fitness Rate 43%
Initial Points 32 11 12
GEOLOGY 02
B3578 S24678
Fitness Rate 38%
Initial Points 20
Eroded Taxonomy
Conway game of life death | organized by ļ¬ttest to lames
HIGHLIFE
B36 S238
Fitness Rate 29%
HONEYLIFE
B38 S238
Initial Points 20 13 14
Fitness Rate 25%
Initial Points 27
Contiguity Testing
Realizing the forms
As the taxonomic forms took shape, it became imperative to examine the resulting geometrical conļ¬gurations to devise methods for transitioning these geometries from the digital realm to fabrication.
Consequently, it became crucial to subject each form to two distinct contiguity tests. The initial test would eliminate cubes that are only attached to the rest of the structure through their edges, as these components risk detachment. The second test would identify all tangent faces of the cubes and remove them completely to ensure the creation of waterproof and sealed forms suitable for 3D printing.
Final form after optimizing for maximum erosion ļ¬tness
Running 1st contiguity analysis for cubes that are only connected through edges
Selection and elimination of none connected cubes
Final form after the 1st contiguity analysis
Running 2nd contiguity analysis to eliminate any inner tangent faces
Final sealed & water proof form for 3D printing
OUTLOOK
Working with Stable Diffusion, Artiļ¬cial Intelligence, Drawings & Images
Speculative Outlook
As the ļ¬nal stage of the thesis, I embarked on a collection of drawings crafted through collaboration among Eden (Stable Diffusion), Midjourney, Photoshop, and drawings generated from Rhino & Grasshopper.
The process began with Eden requiring two distinct inputs to generate images: one for style and another for concept. The concept primarily inputs drawings extracted from Rhino depicting the previously created eroded taxonomy, with the style continuously shifting.
Both the style and concepts underwent evolution and transformation as the drawing process evolved. Initially, the style relied heavily on Midjourney images but later progressed towards integrating drawings such as Paul Rudolphās highway sketches for Manhattan in 1969.
Subsequently, the style of the images transitioned to emulate the "Nine Islands drawings" by Nemestudio. As the concept settings parameter heightened, the images began to mirror the composition of the drawings provided, aligning with the conceptual framework.
As new drawings emerged, they were reintroduced into the initial input of concepts and styles, gradually yielding more favorable results aligned with the desired aesthetic. The showcased images are but a small selection from a total number of 400+ images that helped achieve the desired outcome.
The essence of these drawings lies in their deļ¬ance of conventional approaches to designing spaces,
shapes, and design methodologies. They serve as a deliberate departure from the predictable and structured frameworks often employed in design, opting instead to embrace the inherent unpredictability and dynamism of natural processes. By challenging traditional norms, these drawings open up avenues to explore uncharted territories and break free from the constraints of tradition.
Ultimately, the aim of these drawings extends beyond aesthetic exploration; they serve as a catalyst for reimagining the very essence of design itself. By challenging established norms and celebrating randomness, they pave the way for a more dynamic and inclusive approach to designāone that embraces complexity, embraces change, and draws inspiration from the rich natural world.
In the earlier stages of my research, there was a focus on studying and analyzing Land Art projects. I delved deeply into the work of Michael Heizer, particularly his piece "Double Negative." This installation is composed of two trenches carved into the eastern edge of the Mormon Mesa, located northwest of Overton, Nevada, created between 1969 and 1970.
While "Double Negative" offers a tremendous experiential encounter, with one trench overlooking the other, it also poses a signiļ¬cant disruption to the natural ecosystem. This reļ¬ects on the delicate balance between working with nature and learning from it. True harmony with nature entails not invading or destructing it but rather understanding its inherent principles and applying them thoughtfully in design endeavors.
Nature serves as an endless source of lessons, offering insights into intricately interconnected ecosystems that rely on one another for survival. It represents a complex system that sometimes eludes complete comprehension, yet remains an integral part of a larger ecological network. Embracing these lessons and acknowledging the profound interconnectedness of all elements within nature can guide us toward more harmonious design practices.
Figure 10 Figure 11
BIBLIOGRAPHY
Architecture & Biomimicry
1. Micael Pawlyn. (Oct. 6 2016). Biomimicry in Architecture . RIBA Publishing; 2 edition
2. Steven Johnson. (Sept. 10 2002). Emergence The Connected Lives of Ants, Brains, Cities, and Software. Scribner; Illustrated edition
3. Neri Oxmen. (July 24 2018). The Neri Oxman Material Ecology. The Museum of Modern Art, New York; Expanded, Revised edition
4. Neri Oxmen: (October 29, 2015) Design at the intersection of Technology and Biology: Ted Talk
https://www.youtube.com/watch?v=CVa_IZVzUoc
5. The Art and Science of Ernst Haeckel. (Nov. 13 2017). TASCHEN; Multilingual edition
6. Gary B. Meisner, The Golden Ratio. (Oct. 23 2018). Race Point Publishing; Illustrated edition
7. Michael Hansmeyer: Building unimaginable shapes. (July 27, 2012)
https://www.youtube.com/watch?v=dsMCVMVTdn0
8. Mohsen Mostafavi and David Leatherbarrow, (March 22, 1993). On Weathering The Life of Buildings in Time.
9. Matthew Kirschenbaum, Sand table Granular Worlds: Situating the Sand Table in Media History
23. Jason Warren, Raindrops And Bombs: The Erosion Process. Division of Agricultural Sciences and Natural Resources
24. Soil and Water Conservation Extension Specialist
25. Erosion deļ¬nitions, National Geographic https://education.nationalgeographic.org/resource/erosion/
Falling Sand Game, Cellular Automata & Conway Game of Life
26. Falling Sand Game
The falling sand game adheres to the Cellular Automata principle, where the behavior of each cell is inļ¬uenced by its neighboring cells. It was crucial in the research to commence understanding how we could utilize Conway Game of Life as an erosion mechanism.
29. Conway game of life catalogue for life-like rules
https://catagolue.hatsya.com/rules/lifelike
30. Pinar Calisir Adem, Gulen Cagdas. Yeditepe University, Department of Architecture, Istanbul, Turkey. (November 11, 2020). Computational Design Thinking through Cellular Automata: Reļ¬ections from Design Studios
31. HERR Christiane M. and KVAN Thomas. Using Cellular Automata to Generate High-Density. Building Form. Department of Architecture, The University of Hong Kong, Pokfulam, Hong Kong.
Evolutionary Solver
32. David Rutten, (March 4, 2011). Evolutionary Principles applied to Problem Solving
