PROTOTYPING THE ORGANIC: AI in Design Workflows for Complex Forms in Nature

PROTOTYPING THE ORGANIC

AI in Design Workflows for Complex Forms in Nature

Master in Advanced Computation for Architecture and Design Institute for advanced architecture of Catalonia Master Thesis 2022 Michal Gryko

2022, Printed in Barcelona, Spain For more information on the MaCAD program visit: https://iaac.net/educational-programmes/masters-programmes/macad/ Institut d’Arquitectura Avançada de Catalunya Pujades 102 baixos, Poble Nou, 08005 Barcelona

PROTOTYPING THE ORGANIC AI in design workflows for complex forms in nature

Thesis Submitted to The University of Tokyo In Partial Fulfilment of the Requirements for The Degree of Master in Advanced Computation for Architecture & Design By Michal Gryko Spain, Barcelona September, 2022

Thesis Directed by: David Andes Leon

ACKNOWLEDGEMENT Firstly I would like to give my appreciation to David Andres Leon, the Director of the MaCad Master Program for providing much guidance throughout the thesis. In addition to David’s encouragement and direction throughout the master course, the program allowed me to significantly develop my design related computational skills and knowledge about the industry. Alongside David Andres Leon, I would like to give a thank you to Hesham Shawqy who has also provided technical support on the web development side of work and helped greatly throughout the year.

ABSTRACT The conception of a new design or building is arguably the most creative stage of a project and one that can be most influenced by inspiration from the world around us. Artificial Intelligence (AI) algorithms are being increasing implemented to generate inspirational and creative images, however the extent in which this can be further used to create workable designs is always in question. This paper explores how these algorithms can go beyond creating provoking images to be implemented in a wholesome design workflow that allows non-technical users to configure and output rationalised organic forms rapidly for concept development.

Can the process of designing complex forms for architecture be simplified through AI to enable less technical skilled users to move between inspiration to prototype rapidly? How will the future combine the creative and analytical capabilities of AI for design applications? As machine learning becomes more advanced, creative concept workflows can be streamlined to single mixed-reality platforms to output complex analysis and feasible design options to the average user. Investigations are undertaken on the potential for these workflows to develop within the design and AEC industry.

Key words: 3D DC-GAN, web configurators, geometric optimization, XR visualisation

CONTENTS

INTRODUCTION 1-2

PART I Nature as inspiration 3-10

PART II AI as a creative generator 11-16

PART III Web configurators for design 17-26

PART IV Future interfaces 27-32

CONCLUSION 33-34

Figure 1: Future AI workflow methodology for concept design

01 | Introduction

INTRODUCTION

Future Workflows

The objective of this research paper is to explore whether a user with limited technical or CAD knowledge, can prototype and analyse a complex design at concept stage with simple inputs, aided by artificial intelligence (AI) and configurators. Through the AI interpretation of a selected image or text, the average user can rapidly produce an organic form and be interactively rationalised through Physics simulations,, construction analysis, K-means clustering and other algorithms. These are visualised in the web and can be fully downloadable as a model for further processing or prototyping.

The outcome is a web interface providing the ability to interactively configure geometric complex design while being provided with immediate feedback on technical analysis and constructibility details which will rapidly speed up the design process on a very common typology. As the technology develops, these processes will be able to be run interactively through single web interfaces or directly through mixed reality devices explored in the final chapter (Figure1). Through these studies a cohesive workflow is established into the future of early stage architectural design and prototyping.

Introduction | 02

Figure 2: From Hokusai’s thirty-six Views of Mt.Fuji collection, depiction of Hongan-ji Temple, Asakusa

03 | Part I

PART I

Nature as Inspiration

The relationship between man and nature is at the heart of many cultures.

At the blank start of design projects, there is the most potential to be inspired and draw from the natural surroundings. The artistic creativity and openness to influence makes this stage of the design process vibrant.

Starting Points

At conception, the form of a building is most malleable, with the potential for dramatical change due to the design becoming more concrete and difficult to change further down the design process. However, this flexible stage of the design is also the most time consuming through the process of analysing and developing numerous iterations. This can be most said about innovative and geometrically complex projects, especially those influenced by nature. A certain category of designers and architects particularly draw influence from this area, whether in relation to aesthetics, forms, or philosophy. The traditional methods of form finding for the more organically shaped designs, often emerge from sketches, discussions and model making which can be augmented with more modern methods.

This process is exemplified by numerous architects, designers and artists who draw inspiration and link nature to design throughout the ages.

From the traditional Japanese wood block prints of Hokusai juxtaposing the curve of the steeple rooftop and form of Mount Fuji (Figure 2) to the 21st century freeform buildings of Zaha Hadid which the organic forms can be interpreted to have derived from nature.

Philosophical Links

Many cultures draw from nature and their surroundings as an initial start to influence their designs. Although not possible to draw upon all of them for their vastness, Japanese creative arts and their link with nature is one strong category which reveals a strong appreciation and diverse interpretations. The philosophical thought between form and nature has been questioned and interrogated for centuries. Especially in Nature as Inspiration | 04

Asian cultures through the expression of Buddhist philosophies, nature and architecture are closely intertwined. Kengo Kuma is an example of an architect who takes a different approach with natural inspiration (figure 3). He explains how rather through architecture’s relation with it and harmonious integration there is no ‘reason for architecture to adopt organic forms’ or ‘to be squiggly or to have slimy sheen’. (Kuma, 2009 p.56). Although not geometrical complex, the natural link is evident.

Looking more literally to nature and taking the example of Japanese gardens, prominent natural forms such as waterfalls, ancient trees and boulders are revered. Their forms exemplify these connection through careful selection and choreography.

Particularly with the design of

Japanese rock gardens, depending on the ingenuity of the designer, they symbolise different natural elements and create a different story. The curated rocks create a juxtaposition between natural and rectilinear forms (Figure 4). The sense of beauty that emanates from the use of natural lines and implementation of man-made architectural lines “implies a close affinity with architecture.” (Mansfield and Richie, 2016, p.40-45) Evidently the interpretations of nature are boundless and as a result, the chosen focus is towards proposing a workflow for interpreting natural organic forms geometrically in the application to architecture. Moving from the natural form to the architectural form, an increased complexity arises through the need to rationalise in order to meet larger construction scales and building functions.

Many architects and designers take

inspiration from nature for their designs and interpret them in vast ways from the philosophical, scientific, the poetic and to more literal organic form extraction. The realization of these forms is where engineering and design is pushed to make it feasible for construction.

05 | Part I

Geometrical links

Figure 3: Kengo Kuma’s bamboo tea pavilion, north of Beijing China 2002 and his minimalist approach to design and integration with nature

Figure 4: Largest stone garden in Japan at Kongobun-ji Temple on Mount Koya

Nature as Inspiration | 06

These processes extend to form relaxing, segmentation into panels or constructable elements, functionality and material suitability. (Helmut Pottmann et al., 2007, p.671) Although the right geometry cannot provide a solution to the complete process, understanding and assessing the Degrees of freedom and curvature for shape optimization is crucial in the workflow. In regards to realising large scale complex shapes, emulating nature to find the most natural form is often the most efficient. Through form finding techniques taking advantage of natural forces, complex calculations can be avoided. Modern software and physics simulation software can very easily perform these operations but even prior to this technology, physical modelling from Gaudí’s and Frei Otto’s chain models delivered structurally optimized shapes.

In conjunction with the form, the materiality and suitable structure all need to be assessed together. The ability for engineered wood to be used to create curvilinear forms and as forest product particularly amplify the relationship with nature and therefore take focus as a material for the proposed workflow. Architects Shigeru Ban and Frei Otto exemplify this strategy through their works and celebration of these complex wooden structures.

For Shigeru Ban, his architectural approach is that of the “invisible structure” through incorporation of the complex structures into the design.

Many of his more recent buildings such as the Nine Bridges Country Club and Pompidou-metz centre (Figure 5) employ heavy timber sections digitally fabricated in contrast to “Otto’s lightweight principle at the core of bending-active lattice shell structures” (Fabio Bianconi and Filippucci, 2019, pp.198–200).

However both these approaches demonstrate an

undeniable affinity with nature and expression of the organic.

07 | Part I

Material and Structure

Figure 5: Shigeru Ban’s Nine Bridges Country Club and integrated organic glulam structure

Figure 6: Zaha Hadid’s Dongdaemum Design Plaza consisting of over 45,000 panels in varying degrees of curvature and sizes

Nature as Inspiration | 08

Aside from gridhshell and glulam structures, the segmentation of complex forms through panelization in various materials is another diverse design strategy. Whether through the expression of form and light through the steel and glass designs as demonstrated by Massimiliano Fuksas or the curvaceous ceramic and aluminium panelization of forms by Zaha Hadid Architects (Figure 6), they both demonstrate a remarkable expression of the organic.

Patrik Schumacher explains how Zaha Hadid Architects approach form creation through “form-to-program heuristics” whereby form selects the function instead of the other way around. This process places an emphasis on form finding and on post-rationalization to create remarkable architecture. Although not philosophical inspired by nature the fluid nature of the forms implicitly take reference from it (Figure 7).

Due to the complexity and variety of architectural styles and interpretations as discussed in this chapter, an exploration is taken through more specific styles and that take a more direct geometric link with natural organic forms exemplified by the works of Shigeru Ban and Zaha Hadid. This paper focuses on developing an AI workflow creating rationalised designs for contemporary architecture, with particular emphasis on expressive freeform wooden, structures and additional penalization strategies (Figure 8). The key to the proposed workflow is the adaptability and potential to expand to any design niche over time.

09 | Part I

Style Selection

Figure 7: Forms in nature as architectural inspiration

Figure 8: Naturally inspired organic architecture, design and sculptures

Nature as Inspiration | 10

FIGURE 9: Midjourney research lab utilising artificial intelligence to creates images from textual description: Mountain in the style of Shigeru Ban

11 | Part II

PART II

AI as a Creative Generator

2D Limitations

Recently the proliferation of artificial intelligence in design, particularly for creative image generation through applications such as MidJourney and DALL-E, has resulted in vast explorations involving 2D inspirational imagery (Figure 9). However, beyond exciting and inspirational image generation, questions are often asked about the next development step and how three-dimensional designs be created from these for physical world use. The implementation of this technology beyond inspirational imagery requires further investigation in order to be rationalised and prototyped into working designs.

The ability for anyone with access to an internet browser to generate complex and imaginative scenes through simple text has brought large international attention to the potential of 2D Generative Adversarial Networks (GAN) algorithms to creatively output in any specified style.

As the accuracy of these algorithms improve there is potential to move beyond inspiration and toward practical applications of architectural creation.

3D Generation

Notably within the realm of 3D GAN model generation, development in this area is transforming the creative industries through the creation of tangible objects. Although results for the training of 3D GANs are still in their primordial stages and generated meshes can be quite coarse in the deployment of algorithms that use Voxelisation (NeurIPS 2016) or point clouds for generation (ICML, 2018) newer algorithms are creating much finer and accurate representations.

Applications which are pushing the boundaries of photo to 3D algorithms using convolutional neural nets include Nvidia’s GANverse 3D (Figure 10) and a Bristol University startup spin-off called Kaedim (Figure 11) which both target asset creation for games and movies. They each boast AI as a Creative Generator | 12

the ability to create texture 3D models based on just single or multiple images of an object. Although these services are targeting the games industry and 3D asset creation as a service, there is much potential to expand into other industries.

Supplementing this image to 3D approach, there are currently investigations to develop applications that allow 3D content generation from text prompts. One example is provided by software developer Andew Heumann who’s integration of AI into the generative building web platform Hypar, delves into the potential to create architecture from text (Figure 12).

The capability for a user to describe a building or design

that they desire and have a 3D model generated will open up many intelligent and rapid workflows in the future. The speed and ease at which to generate these 3D assets has potential to go beyond games and fantasy to create constructable objects.

As a basis for this investigation, various methods were tested to try and generate a complex architectural 3D model from a 2D or text input (Figure 13). In order for this architecture to be created an input needs to be generated by an AI algorithm to create either a closed curve or mesh which can be processed with further steps. Three different approaches were explored using existing AI web applications and computer vision libraries in combination with other 3D tools. The first approach employs computer vision for image segmentation and contouring with the python library scikit-image to create a contour of the organic shape (Figure 13-a).

The contour is converted to a spline within the 3D software Rhinoceros and the form is created through use of the physics simulation plugin Kangaroo by anchoring the spline, creating a flat mesh and applying gravity vertically to inflate the mesh. From this mesh various structures and panelization can be applied and analysed. This simple approach 13 | Part II

2D to 3D Exploration

FIGURE 10: NVIDA’s GANverse3D which creates virtual replicas with lights, physics models and materials from photos of cars

FIGURE 11: Kaedim’s web application for image upload and textured 3D asset creation

FIGURE 12: HYPAR’s text to 3D for architecture within a web app AI as a Creative Generator | 14

has many limitations as the input image gives no indication of the 3D form only the outline however. Rather, this approach could be useful for adding dimensionality to plans or simple designs with boundary outlines.

The second approach investigates a combination of Text to Image generation and Monocular depth estimation algorithms to create a depth map. With this, the image can be three dimensionalised with 3D software such as Houdini (figure 13-b). This method is based on a workflow utilised by Shane Bugni and Gabriel Esquivel at Texas A&M University interpreting layered generative adversarial networks within an integrated parametric process of three-dimensionalisation. The workflow is effective at augmenting the 2D GAN generated images such as from MidJourney to give a more in depth understanding of the architectural form. The draw back still remains that the model is more sculptural and inspirational rather than transferable to a build-able architecture.

The final workflow explored, utilises the web application Kaedim to directly generate a closed Mesh from a image (figure 13-c). Image inputs are required to contain a clear single object with a plain background to help with the object identification. Multiple images of the same object from different angles can aid the process, although individual images suffice.

More creative forms can also to be created from text inputs if combined with other image generating AI algorithms such as MidJourney (Figure 14).

The results become more unpredictable as the unique forms are derived from a mix of creative images rather than distinct images of nature. The output from the final workflow proved the most promising as the forms generated have volumes which show potential to be processed further.

For this reason, this process is the primary driver for the AI generated massing to be processed in the next chapter to rationalise and analyse the forms into constructable buildings.

15 | Part II

Kaedim

a Image to 2D spline

IMAGE SEGEMENATION AND CONTOURING

b

Text to Image MIDJOURNEY ARTWORK FROM TEXT

c Image to Mesh

KAEDIM IMAGE TO 3D ASSETS

AI Nature

Stone

Forest

FIGURE 13: Three approaches to using AI for 3D form generation

FIGURE 14: Kaedim 2D to 3D mesh generation using natural examples

AI as a Creative Generator | 16

FIGURE 15: Surface to relaxed mesh, to rationalized gridshell design process by designproduction GMBH for the Centre Pompidou-Metz. Designed by Shigeru Ban Architects

17 | Part III

PART III

Web Configurators for Rationalization

Form to Architecture

With the base form generated, the next step asserts the users’ agency to manipulate the form for it to become architecturally feasible. The AI output currently lacks any scale, geometric and structural understanding.

As a result, human input or decision making is still required to transform the design to reality.

The modelling software Rhinoceros 3D and its vast collection of parametric plugins are popular tools in the architectural industry for advanced geometric optimization and analysis. Along with its interoperability with many other platforms, it provides a good strategy to combine with AI form generation to make constructable architectures. Therefore, this is the primary chosen software for this stage.

Geometric Rationalization

Following the selected organic design styles identified in the first chapter, a parametric script was created to allow the creation of core features which the user can control to create their design options. These construction styles are separated into two categories for simplicity and demonstration purposes: gridshells and panelization.

The lightweight strategy of employing timber gridshells and lattice structures reveal a beauty through the exposed structure which emphasises the form. The ability to form a single entity adds to the connection of organic objects. Aside from the aesthetic use of these structures, there is a significant amount of engineering knowledge necessary to realise them with the core “structure concentrated into strips”. (Chilton and Tang, 2017 p.33) In relation to the sizing of wooden members, they may be continuous, crossing each other at nodes or short in length, passing from node to node. In addition, the grid may have single or multiple layers. For the Web Configurators for Rationalization | 18

purpose of this workflow, continuous long members are formed and optimized from a initial flat square grid which is naturally deformed by gravity through equal loading over the selected form. The use of the Plugin Kangaroo allows this optimization process with much ease. This also emulates a similar on-site “top-down” gridshell forming technique whereby the flat grid is lowered down over scaffolding towards a perimeter while the shell is manipulate into shape under gravity as demonstrated in the Downland Gridshell project. (Adriaenssens et al., 2014, p.89, 115) The second broader category of panelization, allows the segmentation of complex forms to more closely follow the original form and potential for a large range of sizes, panels types and materials. The majority of these panelling solutions require a primary supporting structure through gridshells, truss or beams structures. Typically, glass panelling solutions require a steel grid-shell that lies beneath which would be optimized in similar methods to timber gridshells, however for this exercise the focus is just the panels themselves.

Options allow for configurations of sizes, types and grouping to employ different analysis techniques. In particular, selection features will prioritize grid types and optimal grouping with k-means clustering in grasshopper to give an idea of types and numbers to the designer.

With a strategy in place within Rhinocerso 3D to create the architectural solutions, a key component to linking with the AI form generation is the ability to configure these designs without technical expertise and on a simple interface. This is where rhino.compute technology comes into play. Rhino.compute developed by Robert McNeel & Associates allows to create a headless version of rhino, to run grasshopper scripts and access to rhino common geometry within the web with other front-end 19 | Part III

Rhino on the web

FIGURE 16: Tech stack: Rhino.compute used to run grasshopper definitions on the cloud with a back-end node.js server and rendered on the front-end with three.js libraries.

FIGURE 17: Web interface deployment from AI generated mesh to a configurable web interface

Web Configurators for Rationalization | 20

libraries including three.js (Figure 16). It provides the ability to perform complex geometric calculations through a cloud based stateless REST API. With this web technology, the AI generated mesh as demonstrated with Kaedim can be uploaded into a web browser and processed with a grasshopper script through the use of rhino.compute hosted on a cloud service such as AWS and an app server which runs the grasshopper scripts. These back-end services are interfaced with typical front-end web libraries including three.js to both interface and interact with the 3D objects through simple buttons and sliders (Figure 17).

The aim in the future would be within one web interface to produce a form based on a image or text input that can be rationalised, structured, configured and analysed in real-time. The GAN algorithms similar to the ones employed by Kaedim could be integrated directly into the UI so only a text or image would need to be input at the start. This would provide a user with no technical knowledge to receive basic statics on the design.

This would be an invaluable asset to be able to check early on what designs are feasible and what can be developed further.

In demonstration for proof of concept, a functioning web UI is created incorporating an AI generated Mesh and design configuration categories.

Four different meshes generated using the Kaedim algorithms are used as test subjects (Figure 18). Currently the mesh needs to be uploaded manually however in future development the AI mesh generation will be within the same interface rather than outsourced to other applications.

The interface is separated into three primary sections on the left side menu. The first section in the future will allow for either an input image, text or both to be uploaded as a prompt (Figure 19). This is a similar input technique to other applications such as MidJourney and Dall-E 2, 21 | Part III

Deployment

Mountain Forest Stone AI Nature FIGURE 18: Four meshes generated from images interpreted by AI algorithms and rationalised through a web configurator

Web Configurators for Rationalization | 22

only resulting in a 3D mesh from the inspiration which can be immediately processed.

After generating and viewing the mesh, it can then be

configured into the architectural design required.

The next two sections are for either creating a gridshell design or panelizing the form as previously elaborated on. Although currently the web interface only provides limited rationalization options, these could easily be edited in to future to meet a specific niche or design sector.

For the panelization option, the user can choose between quad, triangular or diamond panels and their sizing (figure 20). The key feature of this section is the ability to group and count the panels to the user preference.

This can be done using the AI algorithm k-means to cluster by similarity of shape or manually through desired amounts. All this data is output beside the 3D visual to show the configured options and sizes.

Similarly when the gridshell option is checked, a physics simulation is run to create a efficient form with approximates the original mesh from a grid (figure 21). This grid can be manipulated in size and rotation. The results can be seen in real-time in the centre of the screen through the use of the three.js library. A data panel beside these outputs reveal key information such as surface area, longest and shortest members and component count.

Additional features include the ability to change views and add layers to provide more context. The final step is the function to download a fully working 3D model and refine, process or visualise it through other software for more feasibility development. As this application develops it will provide more fabrication information such as materiality and specific constraints to that choice along as customizing other design styles.

23 | Part III

Web UI

FIGURE 19: AI generated raw mesh

FIGURE 20: Tri-panelized and k-means evaluated option

FIGURE 21: Relaxed mesh and 2 meter gridshell option

Web Configurators for Rationalization | 24

FIGURE 22: Design iterations on the Web

25 | Part III

Iterative Design

With just the four meshes, dozens of options can be configured rapidly and output both visually with data and 3D model creation. This demonstrates the potential as a rapid prototyping and design tools for early design stages (Figure 22). Only a select few features were included in the web interface for demonstration purposes, as there are countless possibilities of geometric manipulation and analysis to be performed within rhino. compute.

A few immediate improvements that could be added to the grasshopper definition could include the capability to scale and gain a better understanding of constructability.

Additionally, by providing various

component size and type configurations, they could be connected to active manufacturers and suppliers to get cost estimates. Designs could also benefit through additional analysis such as environmental and structural to add optimization capabilities.

Web Configurators for Rationalization | 26

FIGURE 23: Augmented reality used in the construction of the Tallinn Biennale Steampunk pavilion

27 | Part IV

PART IV Future Interfaces for prototyping

XR Visualisation

As AI algorithms become more advanced and portable technologies such as mixed reality devices develop to run all these processes real-time, users will move from 2D screens to wearable devices. This integration is another promising technology workflow for many industries.

The

processes described in the previous chapters have the potential to be delivered within these reality devices to prevent the need for separate platforms and to project concept designs directly into our reality.

Within these headsets the average user can use speech prompts to generate various options that are architecturally feasible with buildability and cost data. This would ultimately save large amounts of time in early stages while reducing the technical need for the average user.

Examples already over the last few years have been built demonstrating the potential for AR technology in both visualisation and construction.

One well published example is of the Tallinn Biennale Steampunk pavilion with the use of AR to construct manually a complex wooden structure.

Igor Pantic, co-designer of the pavilion describes augmented reality as a subset of technologies which belongs to extended reality (XR). They change the perception of reality as a tool to help engage the user whether through digital overlays and interaction. Or this includes virtual reality (VR) on the other spectrum in which the user is completely immersed within. (Interview with Igor Pantic 2022) In this sense, the ability to preview live configurations of concept designs on site or to help in the prototyping in early stages would be invaluable.

The focus for the project was to exemplify material explorations and craft, extended with XR technologies, namely using Fologram which is a Plugin in Grasshopper for connection to the Hololens augmented reality headset (Figure 23). In a podcast interview with Igor Pantic, he explains how the Future Interfaces for Prototyping | 28

architecture industry is notoriously slow for adopting any technology and it is also with the strong belief in a technology and making it accessible to everyone that will push it into mainstream. Through the combination of commercially accessible XR devices and the rapid configuration and AI aid of design, a new workflow towards architectural design could be proliferated for real-world applications and to inhabitable structures.

When integrating these technologies for architecture, game engines are

Game Engines

particularly dominating and driving the field with companies such as Unity and Unreal. Real-time capabilities can be combined with designs and various environments to effectively communicate proposals.

Director of interactive visualisation at SHoP architecture, Adam Chernick who specialises in AR/VR and metaverse research elaborates in a podcast interview on how more so than visualization, the tools are for communication.

They give an understanding of what the client or

designer is going to get faster and more comprehensively.

It offers the capability of finding problems in a design before they arise or get too far ahead when they are difficult to change. In addition to having design data and analysis overlaid in 3D and in reality as shown in the web interfaces in the previous chapter, it can be also used to augment design validation. For early design stages, the ability to view the various options on site gives a much more deeper comprehension, cutting down the amount of time to make a decisions.

For these reasons this technology is an ideal future medium to express the design and communicate effectively and transparently the spatial understating of massing, with the impact of chosen structure and material.

Figure 24 illustrates the potential for a heads up display overlaying 29 | Part IV

AI to XR

OPTIONS

Surface Area: 8237m2 Floor Area: 1800m2 Member Number: 81 Longest length: 75m Shortest length: 6m Approx cost: £4.8M Grid: Regular 1 metre Member sizes: 80 x 50mm Larch lathes

FIGURE 24: Potential Heads-up display with data visualizations for options generated from speech prompts

Future Interfaces for Prototyping | 30

FIGURE 25: Design options and analysis

31 | Part IV

various panelization options and analysis within various environments to get immediate feedback.

The design options generated from the AI and web app configurations are integrated into both AR and VR environments to showcase how all these elements could be combined together. The impact of seeing design, data, analysis and 3D architecture as an immersive experience would greatly augment the proposed workflow.

Future Interfaces for Prototyping | 32

33 | Conclusion

CONCLUSION

Technological Advancement

Will the ability for a designer to verbally describe their design intention and be able to see feasibly design options before their eyes become common place in the future? Would this rapid design develop through the aid of AI and human configuration and replace traditional workflows for early stage design or result in a decrease in architectural quality?

Ultimately, agency will always lie with the designer and the use of AI as a tool to interpret design inspirations should be welcomed as a useful aid.

The demonstrated workflow in this paper only illustrates a small sample of design niches and capabilities due to the breadth of the topic across the four chapters, however it reveals the potential for such tools to develop, revolutionise and proliferate in architectural design.

Future improvements would include addressing specific design or construction niches, adding more material limitations and analysis such as structural and curvature analysis for better fabrication understanding.

All this would be developed comprehensively to allow these processes to take place within one immersive platform without the need to change software. The extra level of integration with geolocators and site context brings the workflow even closer to producing better feasibility studies.

With analysis features such as physics mesh relaxation and k-means clustering displayed automatically, it will provide an idea of the complexity and feasibility of the design generated from AI. This will help advance the shift from pure imagery to build-able architecture.

Conclusion | 34

REFERENCES Bibliography Achlioptas, P., Diamanti, O., Mitliagkas, I. and Guibas, L. (2018). Learning Representations and Generative Models for 3D Point Clouds. Ithaca: Cornell University Library, arXiv.org.

Charleson, A. (2018). Structure as Architecture : a source book for architects and structural engineers. Routledge.

Chilton, J. and Tang, G. (2017). Timber gridshells : architecture, structure and craft. London ; New York: Routledge.

Fabio Bianconi and Filippucci, M. (2019). Digital wood design : innovative techniques of representation in architectural design.

Cham, Switzerland: Springer, pp.198–200.

Hadid, Z. and Schumacher, P. (2011). Total_Fluidity. Vienna Springer Vienna.

Helmut Pottmann, Asperl, A., Hofer, M., Axel Kilian and Daril Bentley (2007). Architectural geometry. Exton, Pa.: Bentley Institute Press, p.671.

Henriques, G.C. and Franco, J.M. (2021). Gridshells: integrating design with structural performance in early design stages, using formal and informal knowledge. Gestão & Tecnologia de Projetos, 17(1), pp.81–95. doi:10.11606/gtp.v17i1.183507.

Kapp, K., Gain, J., Guérin, E., Galin, E. and Peytavie, A. (2020). Data-driven authoring of large-scale ecosystems. ACM Transactions on Graphics, 39(6), pp.1–14. doi:10.1145/3414685.3417848.

Kuma, K. and Kuma (2009). Studies in organic. Toto, Tokyo: Toto Shuppan.

Mansfield, S. and Richie, D. (2016). Japanese stone gardens : origins, meaning and form. Tokyo ; Rutland, Vermont ; Singapore Tuttle Publishing, p.32.

Marc Wilhelm Lennartz and Jacob-Freitag, S. (2016). New architecture in wood : forms and structures. Basel: Birkhäuser, Cop. Menges, A. Schwinn, T. and Oliver David Krieg (2017). Advancing wood architecture : a computational approach. Oxon: Routledge, Adriaenssens, S., Philippe Block, Diederik Veenendaal and Williams, C. (2014). Shell structures for architecture : form finding and optimization. London ; New York: Routledge/ Taylor & Francis Group.

Sasaki, M. (2005). Flux structure. Tokyo: Toto.

Stanislas Chaillou and Birkhäuser (2021). Artificial Intelligence and Architecture From Research to Practice. Basel Birkhäuser Verlag Gmbh.

Wu, J., Zhang, C., Xue, T., Freeman, W.T. and Tenenbaum, J.B. (2017). Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. Ithaca: Cornell University Library, arXiv.org.

Websites aiarchitects.org. (2021). (BE)2 Gabriel Esquivel and Shane bugni. [online] Available at: https://aiarchitects.org/portfolio/esquivel_bugni/ [Accessed 24 Aug. 2022].

Associates, R.M. & . Compute: Features. [online] www.rhino3d.com. Available at: https://developer.rhino3d.com/guides/compute/ features/ [Accessed 23 Aug. 2022]. docs.omniverse.nvidia.com. GANverse3D Extension — Omniverse Extensions documentation. [online] Available at: https://docs. omniverse.nvidia.com/prod_extensions/prod_extensions/ext_ganverse3d.html [Accessed 23 Aug. 2022]. hypar.io. Hypar. [online] Available at: https://hypar.io/ [Accessed 23 Aug. 2022]. kaedim3d.com. Kaedim - AI for turning 2D images to 3D models. [online] Available at: https://www.kaedim3d.com/ [Accessed 23 Aug. 2022].

Midjourney. [online] Available at: https://www.midjourney.com/home/. [Accessed 23 Aug. 2022].

OpenAI. DALL·E 2. [online] Available at: https://openai.com/dall-e-2/. [Accessed 23 Aug. 2022].

35 | References

Images Figure 1: By Author Figure 2: Hokusai K: Asakusa - Thirty-six Views of Mt.Fuji - Artelino. [online] Available at: https://ukiyo-e.org/image/ artelino/17639g1#&gid=1&pid=1 [Accessed 23 Aug. 2022].

Figure 3: Asakawa S : Great Bamboo Wall. Kuma, K. and Kuma (2009). Studies in organic / Studies in organic. Toto, Tokyo: Toto Shuppan.pg 37 Figure 4: Mansfield S: Banryutei. Mansfield, S. and Richie, D. (2016). Japanese stone gardens : origins, meaning et form. Tokyo ; Rutland, Vermont ; Singapore Tuttle Publishing, p.32.

Figure 5: Blumer-Lehmann AG : Haesley Nine Bridges Gold Resort. Chilton, J. and Tang, G. (2017). Timber gridshells : architecture, structure and craft. London ; New York: Routledge. pg 292 Figure 6: Bertrand S: zha_dppseoul . Zaha-hadid.com. (2009). Dongdaemun Design Plaza - Architecture - Zaha Hadid Architects. [online] Available at: https://www.zaha-hadid.com/architecture/dongdaemun-design-park-plaza/.

Figure 7: Kyoto Free Photo. Eikando temple (Kyoto) - Let’s travel around Japan! [online] Available at: http://www.travel-aroundjapan.com/k62-27-eikando.html [Accessed 23 Aug. 2022].

Davidson K. In Pacific nation of Palau, Jellyfish Lake losing namesake. [online] The Jakarta Post. Available at: https://www. thejakartapost.com/life/2016/05/03/in-pacific-nation-of-palau-jellyfish-lake-losing-namesake.html [Accessed 23 Aug. 2022].

Nagoya T: A picture of Mount Fuji. Wikipedia. (2022). Mount Fuji. [online] Available at: https://simple.wikipedia.org/wiki/Mount_ Fuji#/media/File:Kodaki_fuji_frm_shojinko.jpg [Accessed 23 Aug. 2022].

Galina Sandalova Single Boulder Adobe Stock. (n.d.)[online] Available at: https://stock.adobe.com/ search?k=%22single+boulder%22 [Accessed 23 Aug. 2022].

Figure 8: Chilton, J: Savill Garden. Chilton, J. and Tang, G. (2017). Timber gridshells : architecture, structure and craft. London ; New York: Routledge.

Swatch: Swatch Headquarters in Biel. World Architecture Community. (n.d.). Shigeru Ban [online] Available at: https:// worldarchitecture.org/wa-top-teaser/eezvczzh/shigeru-ban-creates-one-of-the-world-s-largest-timber-structures-for-swatchheadquarters-in-biel [Accessed 23 Aug. 2022].

Kengo Kuma Associates: Botanical Pavilion. World Architecture Community. (n.d.). [online] Available at: https://worldarchitecture. org/article-links/egfvm/kengo-kuma-and-geoff-nees-install-botanical-pavilion-creating-tessellated-interior-at-ngv-triennial.html [Accessed 23 Aug. 2022].

Contemporist: New Bamboo: Contemporary Japanese Masters. Dave (2008) [online] . Available at: https://www.contemporist.com/ new-bamboo-contemporary-japanese-masters/ [Accessed 23 Aug. 2022].

Figure 9: By Author Figure 10: Kaedim - AI for turning 2D images to 3D models. [online] Available at: https://www.kaedim3d.com/ [Accessed 23 Aug. 2022].

Figure 11: Nvidia. GANverse3D Extension — Omniverse Extensions documentation. [online] Available at: https://docs.omniverse. nvidia.com/prod_extensions/prod_extensions/ext_ganverse3d.html [Accessed 23 Aug. 2022].

Figure 12: Heumann, A. (2021). openai API into a Hypar function | LinkedIn. [online] Linkedin.com. Available at: https://www. linkedin.com/in/andrew-heumann-13751414/recent-activity/ [Accessed 23 Aug. 2022].

Figure 13-14: By Author Figure 15: designtoproduction GmbH.: Component grid for gridshell surface. Chilton, J. and Tang, G. (2017). Timber gridshells : architecture, structure and craft. London ; New York: Routledge.

Figure 16-22: By Author Figure 23: Bennets P, Kim H: Steam punk pavilion.C3DIZ. (2019). [online] Available at: https://www.c3diz.net/steampunk-paviliontallinn-architecture-biennial-2019/ [Accessed 23 Aug. 2022].

Figure 24-25: By Author

Podcasts and Interviews Chernick, A (2022). MaCAD Theory Podcast E14_Games Engines in Design. with Moor S, Tariq M. Jul Pantic, I. (2022). MaCAD Theory Podcast E19_Augmenting Craftsmanship. with Gryko M, Jaramilo P. Jul.

References | 36