Rumoer 76 Generative Design by RuMoer

periodical for the Building Technologist

76. Generative Design

www.codebale.studio

@codebale

Cover page description This data sculpture is a site specific installation that encapsulates the hidden relationships between the city's climate and the urban morphology at any specific location within the city. In this particular instance, the differential amounts of solar insolation at the street level due to the existing foliage and variations in building volumes is captured. This information is simulated for every daylight hour for the whole year and averaged out for a monthly animation that records the change in insolation on a typical day of the month. An imaginary grid is superimposed on all the streets and these rectangles are extruded in response to the intensity of solar insolation at that particular location. The end effect of this transformation are 12 unique animations that let the viewer virtually experience the change in solar intensity during the course of a year by observing the indirect effect of this in their immediate vicinity in physical space.

Codebale Studio Codebale studio is founded by Ashwin Iyer and Karthik Dondeti based out of Bangalore, IN. We are a generative art studio exploring the relationships between humans, machines, data and art. Our expressions take on various forms across media such as, Generative art and design, Data driven art narratives, Interactive media installations, Adaptive branding and Interactive e-education content. Our work is primarily driven by data that makes it feasible to design site-specific generative art that responds in real-time to the person interacting with it.

RUMOER 76 - GENERATIVE DESIGN 4th Quarter 2020 27th year of publication Praktijkvereniging BouT Room 02.West.090 Faculty of Architecture, TU Delft Julianalaan 134 2628 BL Delft The Netherlands tel: +31 (0)15 278 1292 fax: +31 (0)15 278 4178 www.praktijkverenigingbout.nl rumoer@praktijkverenigingbout.nl instagram: @bout_tud Printing www.druktanheck.nl ISSN number 1567-7699 Editorial Committee Aditya Soman (Editor-in-Chief) Daphne de Bruin Diederik Jilderda Eren Gozde Anil Fawzi Bata Sarah Hoogenboom Sophie van Hattum Tim Schumann Cover Page Original Generative Artwork by Karthik Dondeti Codebale Studio RUMOER is the official periodical of Praktijkvereniging BouT, student and practice association for Building Technology (AE+T), at the Faculty of Architecture, TU Delft (Delft University of Technology). This magazine is spread among members and relations.

Circulation: The RUMOER appears 3 times a year, with more than 150 printed copies and digital copies made available to members through online distribution. Membership Amounts per academic year (subject to change): € 10,- Students € 30,- PhD Students and alumni € 30,- Academic Staff Single copies: Available at Bouw Shop (BK) for : € 5,- Students €10,- Academic Staff , PhD Students and alumni Sponsors Praktijkvereniging BouT is looking for sponsors. Sponsors make activities possible such as study trips, symposia, case studies, advertisements on Rumoer, lectures and much more. For more info contact BouT: info@praktijkverenigingbout.nl If you are interested in BouT’s sponsor packages, send an e-mail to: finances@praktijkverenigingBouT.nl Disclaimer The editors do not take any responsibility for the photos and texts that are displayed in the magazine. Images may not be used in other media without permission of the original owner. The editors reserve the right to shorten or refuse publication without prior notification.

Interested to join? The Rumoer Committee is open to all students. Are you a creative student that wants to learn first about the latest achievements of TU Delft and Building Technology industry? Come join us at our weekly meeting or email us @ rumoer@praktijkverenigingbout.nl

76 | Generative Design

CONTENT

Articles 06 . 26

A Human-centric approach towards Scientific Design -Ir. Shervin Azadi with Dr. Pirouz Nourian , TU Delft. In pursuit of deep architectural design -Pedro Veloso with Jinmo Rhee , CMU.

BouT 82

Board 26 passes the baton ... -Anagha Yoganand , BouT.

Companies 26

22 | Gameplay with encoded architectural tilesets

Generative design as a service -Ondrej Veselý , with Divaye Mittal , OMRT.

40 Generative design with HYPAR -Anthony Hauck, Andrew Heumann, and Tyler Goss , HYPAR. 66 Personalised Generative Design -Cesar Cheng, Sayjel Vijay Patel , Digital blue foam. 74

Data-driven design for complex, multi-disciplinary projects

- ir. Jamal van Kastel with,ir. Jeroen de Bruijn, Royal HaskoningDHV.

Interviews 49

The NEXT steps in design -Sanne van der Burgh & Leo Stuckardt, MVRDV.

Projects

66 | Architectural lessons from Topology Optimization 4

Gameplay with encoded architectural tilesets

- Eleni Chasioti, The Bartlett: UCL.

Topology Optimization: Architectural lessons from Topology Optimization -Ir. Rick van Dijk , TU Delft.

Editorial

EDITORIAL Dear Reader, It is with great pleasure and enthusiasm that I present my last edition of Rumoer as the editor-in-chief. I would like to express my gratitude and appreciation to all the wonderful contributors, sponsors, and the editorial team members that I had the chance to interact and work with over the course of my tenure. I wish the best to the next editor-in-chief, Eren Gozde Anil, as I know she will

Rumoer committee 2020-2021

continue the growth of this publication in the coming year.

power of the machines to rapidly iterate through the We began the discussions for this issue around the topic

trial and error design process. It combines parametric

of artificial intelligence and its impact on our daily life. It is

design with artificial intelligence and can lead designers

a piece of technology that is rapidly transforming the way

into a process of discovering and exploration of a wide

we live, work, and communicate. Industries around the

range of design possibilities by the means of algortihms

world are experiencing a change in their workflows and

programmed to achieve the design goals. This novel

artificial intelligence is automating many of the repetitive

methodology has the potential to have a large impact

and tedious tasks while also improving productivity.

on the industry in the coming years and can completely

This leads us to our main question for this issue, “How is

change the way buildings are designed and built.

Artificial Intelligence and Generative systems changing the Architecture and the Built Environment industry?

This issue 76: Generative Design offers a glimpse into the impact of this disruptive technology on architecture

The process of Architectural design is iterative, where

by exploring projects, essays and interviews by leading

the designer has to make a series of decisions to arrive

academicians, students and professionals in the field

at a design outcome. This can be a long and sometimes

exploring these technologies.

exhausting process. Decisions taken at any phase of the design can influence countless other aspects of the design and consequently becomes a task of balancing the

I hope you enjoy reading it!

benefits and compromise of decisions. Generative design

Aditya Soman

can transform this process by using the computational

Editor-in-chief | Rumoer 2020-2021

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A Human-centric approach towards Scientific design ir. Shervin Azadi, Dr. Pirouz Nourian, Department of Design Informatics, TU Delft.

Formalization of knowledge within a scientific paradigm unifies sporadic efforts through converging glossary and notation, thus enabling scientists to identify knowledge gaps and discrepancies easier. Formalization reveals potential bridges to various domain sciences and facilitates the utilization of methods that have proven effective in scientific problem-solving. In the case of Architecture and Built Environment, there is a long history of scattered efforts for identifying and formalizing design problems and design methodologies, but the big picture is yet missing. In this short piece, we name and frame some of these efforts to identify their parallels with Mathematics, Computer Science, and Systems Theory, as well as to illustrate new opportunities that methodical design unlocks. Fig. 1: The Generator Project

76 | Generative Design

1. Context: In 1971, George Stiny and James Gips introduced ”Shape Grammars,” which described a syntactical system for producing geometrical configurations from a set of rules and one initial axiom [1]. In their grammar, each rule specifies a geometric transformation by illustrating the initial state (if) on the left side and a final state (then) on the right side. Shape Grammars is reminiscent of the Lindenmayer-System (L-System), which was developed by the biologist Aristid Lindenmayer in 1968 to model the morphology of plants [2]. Both of these formal grammars were focusing on encoding the process of geometric transformation through a grammatical ruleset. Still, they diverge in notation as L-System adopts a string-based notation to describe each transformation while Shape Grammar has moved towards a visual notation. Similarly, in his 1977 book A Pattern Language, Alexander describes an architectural system that consists of a set of local rules in various scales of architectural design. Alexander’s pattern language has inspired other engineering fields on how to encapsulate evidence-based tacit knowledge in system design as well [3]. In the same era, other approaches that adopted the analogy of architectural configuration design with linguistics and graph theory emerged, namely in the avant-garde books of March and Steadman’s ’Geometry of the Environment’ [4], ’Architectural Morphology’ of Steadman [5], and Hillier and Hanson’s ’Social Logic of Space’ [6] that later sparked the umbrella term Space Syntax. The latter especially established the use of the terms syntax and morphology in an obvious reference to linguistics. What is common between their approaches

Fig. 2: The Generator Project: top-left, relation chart of user acitivities inside a residential unit; top-right: Layout, source: MOMA online archive [9]; bottom: Diagram of the system of relations between factors; source: CCA online archive [10]

is a view of architectural configuration as a matter of graph construction. In addition to these, Yona Fridman is arguably the first author to call for a ’scientific and participatory’ approach to architectural configuration based on graph theory in his inspiring book ’Towards a Scientific Architecture’ [7]. In retrospect, all of these approaches can be seen to have been inspired by the influential work of Noam Chomsky on Generative Grammars [8].

shift from design as a matter of drawing toward design

and Norbert Wiener, in 1976, Cedric Price and John

as a matter of decision-making. This crucial thread is

Frazer formulated a system theoretical framework

explicitly present in the Generator Project’s diagram

for a generative architectural configurator called the

of the design process, which is depicted as a data

Generator Project [11]. The design was configured by

flow diagram (see Figure 2). These threads are not

assigning locations to a set of 150 mobile cubes (spatial

independent of each other; a discrete model of space

units) and combining them based on connection rules. In

empowers discrete spatial decision-making (e.g., in

multiple ways, this generator was much ahead of its time

the form of location-allocation problems), generative

by defining a discrete notion of space and addressing

grammars regulate the configuration of modules in a

configuration and shape problems in a single framework.

discrete space, and the combination of decision-making

The Nobel laureate Herbert Alexander Simon eloquently

approach and grammatical structures can modularize

explains the importance of a solid notion of [discrete]

the design process. The crucial role of these reciprocal

space in his famous book the Sciences of the Artificial:

relations will come to the surface as we elaborate on the

”Since much of design, particularly architectural

idea of methodical design.

and engineering design is concerned with objects or arrangements in real Euclidean two-dimensional or three-dimensional space, the representation of space and of things in space will necessarily be a central topic

2. Methodological Design Methodically addressing the societal challenges such as shortage of housing, urban inequality, climate

in a science of design” [12].

crisis, and scarcity of resources within architectural &

A set of common threads are traceable through all of

complexities of design problems; the complexities

these innovative perspectives on design. The foremost is the analogy of architecture to language, which seeks to distinguish morphology and syntax respectively for the study of architectural forms and configurations and grammatical rulesets for systematically defined architectural schools such as classic architecture. The second is the notion of space that lays a foundation for formalizing architectural design as a matter of spatial configuration or formation of spatial boundaries, whether through discretization of space as a grid or modeling spatial relations as a graph. The great advantage of such configurative approaches to design is a paradigm

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Inspired by cyberneticians such as Gordon Pask

urban design processes would reveal human/physical as to which design problems have been referred to as ill-defined [13] or even wicked problems [14]. Due to these complexities, it is generally not an easy task to devise a course of actions that could be guaranteed to reach a single design objective, let alone multiple ones, especially when there is not even a consensus among the involved actors as to what the goals and their priorities should be. In other words, in the presence of complex human decision-processes and multifaceted physical phenomena, the relation between design Choices and Consequences becomes intricate and non-trivial to model, thus demanding approaches

76 | Generative Design

design problems’ underlying complexities (i.e., multidimensional, multi-criteria, multi-actor, and multivalue complexities illustrated in Figure 3). Once a design problem is understood in such a non-reductionist form, it is easy to see the need for (and a current lack of) comprehensive evaluation frameworks capable of encoding, collating, and aggregating domain-specific human/physical knowledge of design quality, e.g., the study of spatial quality as to affordance, ergonomics, and daylight. Fig. 3: the spectrum of complexities involved in built environment design problems Fig. 3: the spectrum of complexities involved in built environment design problems

Generative Design in a broad architectural sense is an umbrella term referring to the science of understanding and converting the problem of architectural design

that take socio-spatial complexities for granted [15], [16]. Such complexity-driven approaches to the study of socio-technical phenomena are generally known as Generative Sciences, advocating the use of network science, Agent-Based Models, Cellular Automata, and in general, stochastic simulations of Multi-Agent Systems for understanding such complex systems [17]. Such complexities have arguably created a knowledge gap concerning ’evaluating design decisions.’ Consequently, there is a common tendency to jump to conclusions in design processes from the abstract desired functionality of a design to its ultimate concrete form, referred to as the ”Logical Leap in Design” [18]. As such, the main objective of methodical design approaches is to bridge this gap by firstly formulating the problem of design, breaking it into smaller formerlyclassified problems, and devising a corresponding course of actions. Subsequently, the methodical design is necessarily tied to a systematic study of 10

to sequences of decision problems, and devising Generative Systems for solving these problems through (q.v. [19] and [20]): •mathematically deriving designs from given design requirements (e.g. in graph-theoretical architectural layout planning [5], topology optimization [21] or shape optimization [22]), •itemising design alternatives through graph grammars (e.g. in [23],[24], [25],[36],[37]) •devising and collectively playing a game with multiple human players to interactively explore choices and consequences in a structured and regulated design process (e.g. in consensual decision-making in multiactor design problems [26], collaborative gamified design [27]) See a spectrum of generative design approaches in Figure 4. In a broader scope, the primary focus of both generative design and Generative Sciences is on

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understanding and managing the non-trivial sequences of choices and their consequences through simulating the dynamics of the underlying phenomena, agents, and their interactions by devising Generative Systems. Epstein emphasizes the explanatory potentials of generative systems as they enable us to artificially simulate the proposed model of a hypothesis and evaluate the similarity of the emergent pattern with the natural one [17]. Ergo, simulation is the critical ingredient of generative approaches as it provides a comprehensive and reproducible understanding of the modelled phenomena that effectively map choices to consequences. In this sense, the scope of generative simulations goes beyond the physical to include human factors for understanding the humaninduced complexities of socio-technical systems such as negotiation dynamics, decision-making processes,

Fig. 4: the spectrum of collective intelligence for spatial design

3.Collective Intelligence Piere Levy defines Collective Intelligence (CI) as a ”form of universally distributed intelligence, constantly enhanced, coordinated in real-time, and resulting in the effective mobilization of skills” [28]. Here we focus on a particular type of CI that emerges from the collaboration

subjective biases, and bounded rationality.

of natural and artificially intelligent agents (q.v. Human-

Figure 4. illustrates the spectrum of technics to generate

side, CI exposes the decision-making processes

designs varying Grammatical Itemization that involves users as the main driving force, to Mathematical Derivation with minimum reliance on user participation; in the middle of which Gamified Exploration is posited as it allows human participants to be the main players while including computational systems to ensure a logical structure and provide objective evaluations of design alternatives as scoring mechanisms. Such a participatory and generative formulation of spatial design problems allows for human and machine agents to interact directly in the design process, hence fostering the emergence of collective intelligence.

based Computation as framed in [38]). On the natural to the participants’ tacit knowledge and insight into societal values. On the artificial side, it exploits the precision, objectivity, and robustness that machine intelligence can bring to the analysis and evaluation processes. The core of such a CI is a shared medium that facilitates communication and allows coordination between all agents by providing an interactive and enjoyable interface for humans from one side and a logical framework for computational agents on the other side. As emerging media dominating the entertainment market, games can provide entertaining and immersive experiences while unfolding the complexity of the relations of choices with prior conditions and posterior

76 | Generative Design

consequences. Besides, through their logical structure, games can fully integrate artificial agents in their system for simulations that can unravel the consequences of choices. As such, games can provide prominent media for engaging participants with complex systems that have emergent characteristics [29]. It is essential to notice that simulation in a more general sense than physical simulations would also mean replicating the decisionmaking dynamics in games (including board games). The term ’simulation game’ as such refers not only to digital simulation games but also to the games or multiactor strategic games that have a complex decision as to their object of focus [29]. Games can implement multiactor play and multicriteria scoring mechanisms thus not only providing for the direct inclusion of participants in decision making. Furthermore, by discretizing and structuring the nature of design decisions, design games also provide for tracking, recording, and studying design decision dynamics. The benefits of structuring decisionmaking processes as games are twofold: on the one hand, the negotiation process finds a rational and transparent basis, and on the other hand, the decision-dynamics can be investigated to extract conclusions in the form of design-principles relating performance indicators to decision-variables. Introducing methods for evaluating the quality of designs alongside the direct inclusion of participants in decision-making will facilitate their direct reflection on the evaluation results. As such, a gamified CI can didactically expose the complex nature of non-linear relations of decision variables Fig. 5: Examples of gamified generative design in student projects: MSc Earthy Design Studio [33], [34]. Image Credits: TerraTetris by Aditya Soman, Vicente Blanes, Christina Koukelli, Neha Gupta, and Dion van Vlarken; Modulabity by Alessandro Passoni, Alessio Vigorito, Fredy Fortich, Kiana Mousavi, and Stephanie Moumdjian

with the performance objectives as well as the human complexity of decision-analysis as to different value systems and the plurality of actors. These potentials

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indicate that gamification can push the design process towards a knowledge-based complex decision-making discourse that contributes to resolving conflicts of goals,perspectives, and interests for reaching inclusive consensual decisions. Consequently, design solutions made through this framework are inherently explainable and reproducible by referring to the series of decisions that participants took and the set of evaluations and analyses that the machine has performed along the process. By explicitly modelling a design process as a complex decision-making process, and thus introducing decisionvariables, the combinatorial nature of the generative design will most likely result in a so-called combinatorial explosion of possible outcomes. Thus, the process of synthesis, i.e. exploring large decision spaces, collating, and drawing a conclusion from multiple analyses, can be overwhelming for humans and demanding for systematic synthesis and search processes. In this regard, algorithms and mathematical procedures can offer Multi-Criteria-Decision-Analyses as well as non-linear Learning methods (typically categorized as Artificial Intelligence) to perform the intricate task of relating consequences to choices (design decisions) to guide such synthesis processes. However, the adaptation and development of AI methods require a formal definition of problems and methodologies that enable objective evaluation, optimization, or adaptation of systems. Especially in use-cases, where framing and formulating problems is challenging due to the double humanphysical complexity of the concerned phenomena, any

Fig. 6: Examples of gamified generative design in student projects: BSc Spatial Computing Architectural Design Studio [35]. Image Credits: CUB3D by Hugo van Rossum, Maren Hengelmolen, Liva Sadovska, and Sander Bentvelsen

machine-generated solution must be not only justifiable

76 | Generative Design

concerning a set of objectives but also explainable [30]

this emerging domain; seeking to contribute to fostering

and interpretable [31] for humans in terms of the clarity

new types of open collective intelligence for responsible

of the reasoning process. As design problems typically

architectural design and holistic analysis of the built

have human-related complexities that lack formal

environment.

definitions, the interpretability of any method that leads to a decision is essential for a CI system. Gamification of design as a design-methodological approach offers mechanisms for supporting ’direct and structured communication’ between human-agents and machine agents, required to foster CI [32], making interpretability

Authors Shervin Azadi and Pirouz Nourian were partially supported by two research grants while working on the content of this article: project EquiCity, Granted by Netherlands Organ-isation for Scientific

easily attainable.

Research (NWO), the grant Idea Generator, Nationale

The participatory generative approach to design as

and project GoDesign, Granted by the Dutch Ministry

facilitated by and structured in games reveals a nonreductionist picture of the human-physical complexity of architectural design processes. Transparently revealing such a complex picture and relating design decisions to their performance consequences not only makes design learnable as a knowledge-based process of decisionmaking aimed at attaining high levels of performance, but also an inclusive social decision-making process that induces a sense of holistic responsibility towards measured social and environmental consequences of long-lasting design decisions. Generative Design Games can enable participants to design effectively and intelligently while respecting societal values and caring for the planet. Participatory Generative Design in Architectural Design is an interdisciplinary field of research that renders a growing list of questions/ problems and answers/solutions. The Laboratory of Generative Systems and Sciences in Architecture and Built Environment GenesisLab, is an open-science initiative for research, development, and education in

4. Acknowledgements

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[27] H. Sanoff, Design Games. Jun. 1978. [Online]. Available: https://www. researchgate.net/publication/317236947 Design Games. [28] P. Levy, P. L´evy, and R. Bononno, Collective Intelligence. Basic Books, 1997, ISBN: 9780306456350. [Online]. Available: https://books.google.nl/ books?id=sjjbAAAAMAAJ. [29] H. K. Lukosch, G. Bekebrede, S. Kurapati, and S. G. Lukosch, “A scientific foundation of simulation games for the analysis and design of complex systems,” Simulation and Gaming, vol. 49, no. 3, pp. 279–314, 2018. DOI: 10 . 1177/1046878118768858. [Online]. Available: https://app. dimensions.ai/details/ publication/pub.1103894130%20and%20https : / / journals . sagepub . com / doi / pdf / 10 . 1177 / 1046878118768858.

1126/scirobotics.aay7120. [31] F. Doshi-Velez and B. Kim, Towards a rigorous science of interpretable machine learning, 2017. arXiv: 1702 . 08608 [stat.ML]. [32]S. Suran, V. Pattanaik, and D. Draheim, “Frameworks for collective intelligence: A systematic literature review,” ACM Computing Surveys (CSUR), vol. 53, pp. 1–36, Feb. 2020. DOI: 10.1145/3368986. [33]Earthy, generative design for earth and masonry architecture, msc3 design studio 2019-20, 2020. DOI: 10.5281/ZENODO. 4297469. [Online]. Available: https : / / github. com / Pirouz -Nourian/earthy 19. [34]Earthy, generative design for earth and masonry architecture, msc3 design studio 2020-21, 2020. DOI: 10.5281/ZENODO. 4297471. [Online].

Available: https : / / github. com / Pirouz -Nourian/earthy 20. [35]Spatial computing: Computational design studio, bsc minor studio: 2020-21, 2021. DOI: 10 . 5281 / ZENODO . 4573290.[Online]. Available: https : / / github . com / Pirouz - Nourian /Spatial Computing Design Studio20. [36] P. Nourian,Configraphics: Graph Theoretical Methods forDesign and Analysis of Spatial Configurations, en. 2016,ISBN:978-94-6186-720-9. [37] R. Oval, "Topology finding of patterns for structural design,"Ph.D. dissertation, Ecole des Ponts - ParisTech, Paris, Dec.2019. [Online]. Available: https : / / block . arch . ethz . ch / brg /publications/1042. [38] A. J. Quinn and B. B. Bederson, "Human computation," inProceedings of the 2011 annual conference on Human factorsin computing systems - CHI '11, ACM Press, 2011.DOI:10.1145/1978942.1979148. [Online]. Available: https://doi. org/10.1145/1978942.1979148.

Graduate

[30] D. Gunning, M. Stefik, J. Choi, T. Miller, S. Stumpf, and G.-Z. Yang, “XAI— explainable artificial intelligence,” Science Robotics, vol. 4, no. 37, eaay7120, Dec. 2019. DOI: 10.1126/scirobotics.aay7120. [Online]. Available: https://doi.org/10.

Shervin Azadi is a researcher at Design Informatics Chair in TU Delft. His main interest lies at understanding the complexities of spatial design problems with regards to multiple actors and criteria involved. Shervin has developed algorithms and tool-sets such as [topoGenesis](https://topogenesis. readthedocs.io) and [emergentium.io](https://emergentium.io/) for spatial analysis and simulations. Nevertheless, his current research investigates the potentials of a mathematical/computational formulation of the spatial design process as a series of decision-making processes, providing for collaboration of natural and artificial intelligence in face of spatial design problems. ir. Shervin Azadi Pirouz Nourian is an Assistant Professor of Design Informatics at TU Delft, the Netherlands. Pirouz has a PhD in Design Informatics (2016), an MSc in Architecture (2009), and a BSc in Electrical Engineering, with a major in Control Systems Engineering (2004). He develops mathematical methods and software applications for design and assessment in the fields of Architecture and Built Environment. Particularly, he researches and develops methods for generative design and spatial computing (geometrical, topological, and graph theoretical computing). In addition, he teaches computational design and procedural 3D modelling in MSc Architecture, MSc Geomatics, and MSc Building Technology at TU Delft. Dr. Pirouz Nourian

www.abt.eu

Image © Ossip van Duivenbode

Building ambitions Do you want to work on leading projects in an organization where development and innovation are of paramount importance? Together with our customers and partners we develop the buildings of the future! We work on projects that matter. Think of the Boijmans van Beuningen Depot, where we calculated the optimal technical shape of the reflective facade. For House of Delft our integral team was able to realize a solid, preliminary design in three months’ time. Knowledge development What characterizes us is our curiosity, our eagerness to learn and our passion for technology. ABT invests in knowledge development and innovation. Building envelope engineering, BIM, concept development, computational design, refurbishment, parametric design and AR: we apply it all in our projects.

Building zero impact With all the engineering disciplines under one roof, ABT can offer - through our integrated design approach - an optimal mix of sustainability measures in the field of energy, water and materials. The result is a healthy building for the user. Working at ABT? Looking for a challenging internship or graduation assignment? Or ready for your first step after your graduation? We are happy to get to know you and curious to see if you are the perfect fit for our team in Delft, Enschede or Velp (Gld.). Find our current internships, graduation topics and vacancies at werkenbijabt.eu. We look forward to your application.

Figure 1. Rendered solutions from the user-defined percentages approach

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Gameplay with encoded architectural tile sets Eleni Chasioti, The Bartlett - University College of London. Our physical surroundings play a significant role in our everyday experiences, encourages certain behavior and affects us both physically and psychologically. Similarly, the virtual world of a video game is driven by “real-world” principles and sometimes simulates many physical limitations. Architecture is usually the scaffolding that allows a game’s narrative to evolve, it orchestrates the actions, provokes the player and helps to create the necessary atmosphere.

76 | Generative Design

The need for detailed and time efficient content

design using the Wave Function Collapse algorithm”, I

generation in games has promoted research that can

explore the utility of a relatively new algorithm called

be proven useful outside of the gaming realm. The

Wave Function Collapse (WFC). WFC is a procedural,

automation of repetitive design tasks, the encoding of

constraint solving algorithm developed by Max Gumin

design principles as well as the exploration of design

(Gumin, 2015) that gained a lot of traction in the gaming

variations are common in both gaming and architectural

community. The goal of the algorithm is to generate

projects.

new images in the style of a given example image while preserving local similarities. The algorithm ensures

So, what if the game and architectural industries have

that every smaller patch in the input image will exist

much more to learn from each other? And what if content

somewhere in the output image.

generation algorithms for games can propose new approaches to generative design?

In simple terms, it performs the following steps: 1. It extracts patches of a defined size from the input image.

Background In my thesis, titled: “Gameplay with encoded architectural tilesets: A computational framework for building massing

2. It converts the patches into indices to make neighborhood constraints checking faster. 3. Starting from a random location in the output image,

Figure 2. Image generation using the Wave Function Collapse algorithm (Gumin, 2015)

Graduate Figure 3. The process of going from an input to a tile set.

it places a randomly selected patch from the input

Texture synthesis algorithms work mostly in 2D using

image. Then, it incrementally builds up the output

pixels to generate or complete images. Generating high

image based on inferred relationships between

resolution textures is an integral task when designing

patches.

digital gaming environments, characters etc.

Procedural Content Generation (PCG) is the automated

Computational Framework

production of different media, media that is usually

The dissertation focused on the implementation of

designated for human production, such as poetry,

the WFC algorithm in 3D and the development of a

paintings, music, architectural drawings etc. Content

computational framework to test the potential of the

generation for video games demands a lot of manual

algorithm in design massing. The implementation was

labor; it is considered one of the main costs in video

developed as a Grasshopper (a visual node-based

game development. With PCG the cost is reduced by

scripting environment) plug-in inside Rhino (a 3D

generating content algorithmically, which demands less

computer aided design application).

human contribution (Barriga, 2019).

The proposed computational framework envisions a

The task of generating images based on an example,

process where designers can augment their design

generally describes the objectives of a wide research

proposals by providing the tool with an example. The

area popularized in the 80’s, called texture synthesis.

tool then would attempt to do the following:

76 | Generative Design

Figure 4. Encoding a tile into a unique numerical representation capturing mainly the state on the peripheries.

1. Segment the example, creating a tile set. 2. Encode

each

tile

into

unique

The WFC starts with random initialization and in case it numerical

fails to produce an output, it automatically restarts (non-

representation. In addition, this step facilitates the

backtracking WFC). The algorithm is adapted to work

detection of unique tiles in the example.

with the information provided from the encoding step,

3. The WFC algorithm reads the encoded input, infers

which is a unique representation of input meshes that

relationships and neighborhood constraints and

takes into consideration connections on the peripheries

produces an encoded output.

of the tile. The output of the algorithm is later de-

4. A decoding step that deserializes the WFC output and converts it back to tiles.

serialized and matched back to the input meshed tile set.

That is based on the idea that designers subconsciously

voxel grid around the input model, which is used to divide

and intuitively use complex relationship constraints

the input to individual mesh tiles. In cases where the input

to create a design example. In a sense, this step is

model is already divided into tiles, the first segmentation

attempting to decode designers’ intent and encoding it

step is skipped, and the tiled input is directly used in the

into a representation that the WFC algorithm can deal

encoding process. After segmentation, the resulting

with. After all the voxels and their enclosed geometries

mesh tiles and their respective voxel exclosures can be

are serialized, this new representation is passed to the

used in the next step.

WFC algorithm.

The next step, encoding, attempts to bring the example

Internally, the algorithm constructs a 3D representation

driven content generation process (that is usually used

of the input where voxels are used as placeholders for

when dealing with images) to working with 3D tile

the tiles, indicating where a tile exists or not in a specific

sets. The usual method followed when using 3D tile

{x,y,z} position in the example space. Each voxel’s

sets forces the user to define manually for each face

identity (a number associated with its position and

of a given tile, which faces on all other tiles it can be

the encoded representation) is related to all the other

connected to, this adds an extra layer of manual labor for

voxels. The WFC infers all the neighborhood patterns

the designer that can impede their creative design flow.

based on a defined neighborhood size and creates a 3D

The proposed encoding process identifies the unique

output using this knowledge, making sure that no tile

states of connectivity of the tiles provided.

ever appears in a neighborhood where it wasn’t observed

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The segmentation process involves the creation of a

before in the input.

Figure 5. Tile set with constrained balcony - house relationships

76 | Generative Design

Results To evaluate the utility of this computational framework in the architectural design process different tests were attempted, drawing from tasks or constraints that designers usually face in their design process. The first explorations were focused on the trait of directionality and how well would it be respected in the output models. Based on an input example model of small size (4 x 4 x 4 voxel space) with specific façade restrictions, a series of output models from the WFC algorithm were produced to evaluate how well it would scale-up (for example 3 x 4 x 12 voxel space) in terms of consistency, variability and flexibility. The algorithm proved capable of preserving the directionality of the input and managed to generate a variety of output models with different sizes. Figure 6. Model generation with facade constraints

Figure 7.Input and output models with user-defined probabilities

when taking issues like structural validity, environmental

specific percentage of each space type within their

performance, cost and others into consideration. In this

design. The following test, focused on varying the

case the algorithm was extended to take as an input with

implementation of the original algorithm, overriding the

each tile a number, this number can represent a metric

probabilities inferred from the input model. One of the

for any of the issues mentioned earlier. For this test the

essential pieces of information extracted from the input

value chosen was the tile’s volume and the objective was

is the probability of a specific tile occurring. In this case

once minimizing then later maximizing the total building

the algorithm was tested with user defined probabilities

volume. Altering the decision making process of the

instead of the ones observed in the input. Based on the

algorithm to incorporate this new requirement, led to

results it was concluded that the algorithm is able to

the algorithm indeed being able to produce successful

work with user defined probabilities. However, being a

results based on user-defined objectives.

Graduate

Sometimes designers are tasked with achieving a

constraint solving algorithm it cannot guarantee that the results will always satisfy the user’s input.

The Wave Function Collapse algorithm shows promise as a tool for early stage architectural design, especially

Finally, an additional piece of information was introduced.

when the stochastic nature of its decision making

In this scenario, the algorithm was asked to minimize or

process gets constrained and directed to serve defined

maximize a value associated with the input model by

design goals. A future research point of interest is

changing its decision when it comes to tile placement,

exploring the combination of

again a feat designers try to undertake in their process

learning techniques operating at the decisional level of

WFC with machine

Figure 8.Tileset with volume percentages

Graduate

the algorithm. By integrating an AI system in the decision making the stochastic nature of the algorithm could be limited and instead different design-oriented goals could be introduced. Conlusions The extensive use of computational design tools in architecture is already a reality. Incorporating algorithms and processes from different research fields opens new paths of design explorations and promotes novelty and creativity. By developing our own tools and optimizing our workflows we can improve both the design process and the outputs. Such interdisciplinary opportunities should be seen as means to strengthen the role of the architect and an opportunity to combine systematic algorithmic thinking with the creative and intuitive nature Figure 9. Input and output models with minimization/ maximization of volume goals.

of architecture. References [1] Barriga, N. A. (2019). A Short Introduction to Procedural Content Generation Algorithms for Video Games. International Journal on Artificial Intelligence Tools, 28 (02), 1930001. https://doi.org/10.1142/S021821301930001 [2] Gumin, M. (2015). WaveFunctionCollapse. Retrieved June 2, 2020, from https://github.com/mxgmn/WaveFunctionCollapse

Eleni’s interest in parametric design and design automation started during her undergraduate studies as an architect back in Greece. She has been mainly concerned with improving the designing process with the integration of algorithmic approaches. Eleni graduated from The Bartlett - University College of London, after pursuing her MSc in Architectural Computation. Her current role is a Computational Designer in the Creative Technologies team at Bryden Wood, where she explores how technology can improve the tools used in the architectural design process. Her thesis at Bartlett looked at utilizing creative, intuitive ways of augmenting the traditional design process and automating the generation of architectural models through the application of constraint solving and example Eleni Chasioti

based generative algorithms. 26

606 Universal Shelving System 27 620 Chair Programme 621 Table

Figure 1. Project site view

Company

Generative design as a service On generative design and experiments with AI at OMRT Divyae Mittal and Ondrej Vesely, OMRT

About OMRT At OMRT we are enthusiastic about using computational tools to make designers' lives easier. Our company is a fast growing startup founded by two TU Delft alumni in 2018 after being disappointed by the inefficiency of the tools used in the AEC project development. We help our clients get more insight into their projects using computational analysis and integrate tailor-made digital tools into their workflow. Case study project Our expertise is utilized on diverse projects of built environment. One such project is Lumiere Towers in Rotterdam, where OMRT was brought on board to assess the environmental impact of the tower to its surroundings. The assessment included analyses like sunlight hours, shadow impact and wind studies. The project demanded tight requirements for each of the above analyses, as per the standards set by Municipality of Rotterdam under the High Rise Vision 2019. The studies included the impact of new towers including The Lumiere, Post, Rise and ASR Projects, which would be each higher than 150 metres. We solved the challenge by studying the impact of new towers around Hofplein using generative design driving the exploration of various performance indicators.

76 | Generative Design

Figure 2: Structure and program layouts generated for various building and circulation types

Generative design

allows us to quickly explore the possibilities of any site

Almost all our projects are driven by generative design.

in the Netherlands.

For the clients, the ability to generate multiple variants, options that they wouldn’t think of or wouldn't have time

As consultants, we often also join in on the projects that

and resources to try out, is why they approach us. It allows

are already past the initial design stage. These cases

them to make the right decisions in the earliest stages

are always interesting, because we still want to be able

of the project development. This not only improves the

to consider as many solutions as possible, while being

performance but also saves on the cost of the building.

limited by the boundary conditions set by the already made design decisions.

For us the best case scenario, as was the case of

Lumiere, is to be present in the project right from the

Take for example automated floor plan generation. You

start. Then we can really focus on questions that have

can generate a completely random floor plan without

the highest design impact, such as the relationship

much of a challenge. But the ability to generate floor

between the massing and the site. We develop our own

plans that adapt to any requirements put by the client,

tool, Ostate, which is linked to databases such as dutch

whether it’s just the irregular building shape, or the exact

zoning regulations, cadastral plots or 3D city models and

type of circulation and size of each room, is what enables

Company Figure 3: Decision impact throughout project’s life-span

us to use our tools in actual, real life projects.

Cloud

Architects often identify generative design methods with

We run a lot of the analysis in the cloud, running Amazon

experimental, out-of-this world looking designs. But for

and Azure servers that do the heavy load for us when we

us, the potential lies in applying it to the most mundane

need them. We build the solutions to integrate everything

things. As designers, we want to spend as much time

into ie. Grasshopper, so we can just upload a study

as possible on the actual design, not finessing layout

that needs to be done from there and continue working

details. That is probably why for example our parking

without waiting for the computation to be finished.

generator is something that clients immediately get excited about. Let them spend less time figuring out how

Smart solvers

to squeeze that one extra parking space in and more time

Industry standard engines like OpenFOAM for CFD are

for making decisions that really matter.

great for final validation of the design, but come with a large performance cost. Inhouse we use FFD (fast fluid

Massive simulation runs

dynamics) to filter the design options before dedicating

Our design studies often require us to do runs of

all that computation power that CFD demands. FFD

hundreds of variants, with the engineer still working

solvers come from the world of real time computing (game

on the project in parallel. What you don’t want to do, is

engines etc.), and are fast thanks to only approximating

to block his machine for the whole day by doing just a

more of the real fluid phenomena. But luckily we do have

couple of simulation runs. Luckily we have a couple of

some people with actual physics degrees on the team

tricks up our sleeve that allow us to cut down the down-

to keep the inaccuracies in check. The Lumiere project

time of waiting for the results.

extensively relied on the combinations of CFD and FFD computation algorithms to create fast results for the client. 31

76 | Generative Design Figure 4: edges2cats[2] and our own daylight prediction model

Figure 5: Ostate urban massing tool

The dashboard, powered with useful statistical charts

A research topic of ours is how we can apply AI models

such as parallel coordinate charts, performance filters

to predict the results of simulations without actually

and sorting capabilities allows for quick comparison of

running them. One of the promising methods, pioneered

different options and swift decision making.

in the AEC by the Theodore Galanos[1] in 2019, is the use of GAN (generative adversarial network) to predict the

Conclusion

image to image mapping of the design and performance.

As computation nerds we all are at OMRT, we hate

This allows for cutting down the computation time by

wasting time being stuck using inefficient workflows.

orders of magnitude (eg. a wind study in milliseconds).

Our chain of digital tools, from generative algorithms to

We can apply this to daylight, wind, shadow or any kind

ability to analyze and compare large numbers of design

of performance simulation that you can map into 2D

options within tight project timelines, allows us to deliver

image space, but we also experiment we using GAN for

design insight to our clients in days, instead of months.

ie. footprint generation. References Presentation Last but the most crucial step of our process is presenting the results comprehensively in the design dashboard. The strength of generative design lies in exploring different options that computational engines can generate within the given restrictions. Thus, the model generates a huge amount of data which is key to decision making in picking the best design option of the lot. We present the data to different stakeholders in a dashboard with intuitive UI. 32

[1] Chronis, A et al., 2020. INFRARED: An Intelligent Framework for Resilient Design. 25th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA 2020), August 5-6, 2020 [2] Christopher Hesse, 2017. edges2cats. https://affinelayer.com/pixsrv/.

Company Figure 6. Project dashboard for the Lumiere projects

Divyae is an all-round developer at OMRT. He is interested in improving user experiences in the built environment through the use of digital technologies. He graduated in architecture from IIT Roorkee, India followed by the Master’s in Building Technology from TU Delft.

Divyae Mittal

Ondrej is a computational developer at OMRT. He has international experience in applying generative design and machine learning to urban design projects. After completing his Architecture BSc in Czech Republic and Germany, he worked as a researcher in Austria. Currently he is finishing his graduate degree in Urbanism and Geomatics at TU Delft.

Ondrej Vesely 33

Academic

In pursuit of deep architectural design Pedro Veloso, Jinmo Rhee, Carnegie Mellon University CRAIDL In the Summer of 2020, a group of graduate students from the computational design program at Carnegie Mellon University with the support of their advisors decided to create CRAIDL, a group dedicated to creative research in Artificial Intelligence (AI). This initiative was not so much a beginning, but a formalization of the collaborative work in creative AI that the members have been developing over the last few years, often together or with researchers from other departments. Consequently, CRAIDL emerged with a consolidated set of research publications and prototypes developed by four designers with a solid foundation in Deep Learning. This group has been developing research prototypes in domains such as Generative Models (GM), Natural Language Processing (NLP), Reinforcement Learning (RL), and Deep Learning (DL). The prototypes vary in objective from robotic painting to multimodal learning for art. The research and prototypes of CRAIDL reflect a common understanding that the current wave of AI will lead to structural changes for creative practices in art, design, engineering, and architecture. At CRAIDL, We understand that designers are crucial for shaping future AI-based practices and so we strategically focus on interdisciplinary work and design experimentation.

76 | Generative Design

Generative Design In this article we will focus on a branch of the work of CRAIDL that addresses generative design in the context of architectural configuration. Generative design is an indirect method of design where the designer employs models that embed some form of decision-making, such as instructions or behaviors, to generate design alternatives. With the current technology, this generation typically relies on a parametric structure, rules, or other mechanisms that are explicitly defined by the designer. However, designers do not always have access to the rationale necessary to create certain types of design. Not surprisingly, designers conventionally rely on their experience and intuition to generate good design solutions instead of looking for explicit design logic. This is where Deep Learning comes into play. DL is the field of AI concerned with using some experience, such as data or simulation, to make a certain model (usually neural networks with multiple layers) learn a specific task. In the case of generative design, we are interested implicit design rationale into our design process.

Figure 1: An Academy of Spatial Agents: agents reacting to randomness and creating a layout of a house in two different environments

Architectural design is a complex activity that addresses

This type of interaction is common in agent-based

wicked problems, so we consider it fundamental to

models, where computational agents interact in a shared

explore the relationships among different design aspects

environment over time in a simulation. The problem is that

that can benefit from DL. In this paper, we will introduce

existing agent-based models are developed for tasks

two projects that tackle distinct facets of design: An

or phenomena outside of the domain of architectural

Academy of Spatial Agents and Deeprise.

design; it is not straightforward how to adapt those

in training neural networks to incorporate an alien or

models to generate feasible spatial representations.

An Academy of Spatial Agents

To address this issue, we created a custom, agent-

In an Academy of Spatial Agents, we explore workflows

based model tailored for the generation of architectural

that support fine-grained and real-time interactions

configurations, using Reinforcement Learning as a

between designers and the generative design process.

virtual academy where we can create and train agents to

As a result, we built an agent-based model to support

In the first prototype of the Academy of Spatial Agents,

real-time spatial exploration. The backend of this

we used a grid representation for states and actions.

model is the policy/behavior learned by the agent during

The agents are polyominoes that represent spatial

training. The frontend is a game-engine, where the

boundaries. They can select cells to expand and retract,

designer can control not only the goal parameters but

which are the building blocks that the agents use to

also the configurations of the agents and environment.

develop more complex moves such as reshaping,

Furthermore, a parametric model is integrated into

moving, or jumping over another agent. Also, these

the game-engine to enable additional control over the

building blocks enable a step-by-step generation that

spatial and constructive elements (windows, walls,

produces partial representations and supports human

etc.).

intervention.

For the example shown in the accompanying images,

Using Reinforcement Learning, the agents are trained

we use 12 agents to represent the design of a house in

in a simulation where they interact with the environment

two different environments. The designer can intervene

and learn to select actions to maximize a cumulative

in the configuration over time, so the agents must react

reward signal defined by the designer. In other words, the

and look for proper spatial configurations. This results in

designer defines what the agents should do by defining

a trajectory where the agents generate multiple layouts

rewards and the agents learn how to do it by exploring

by locally changing their configuration.

possible actions in the simulation. We trained agents

Overall, the real-time interactions with the model enable

on random environments with parameterized rewards

designers to influence the design space exploration,

for areas, adjacencies, and types of room shapes. This

learn with partial representations, and restructure the

can potentially be extended with other goals, such as

design problem according to new insights.

Academic

address certain goals.

preferences, environmental considerations, and spatial metrics. The agent should learn how to properly behave

Deeprise

in different environments, facing different obstacles,

Deeprise is an investigation of generative design

and with different goal parameters.

based on building morphology. The challenge here is

Figure 2: An Academy of Spatial Agents: interaction using a game engine and parametric modeling

76 | Generative Design

to analyze a vast repertoire of building precedents and use the acquired knowledge to explore morphological variations. Typically, high-rise buildings are classified according to features, such as tower shape, base, circulation core, or architectural styles. However, when the analysis considers a large database of buildings, it becomes hard for a human to properly identify recurrent features and define dominant types. Deeprise addresses this challenge using 3 steps: data collection and preprocessing, training, and design application. We

automated

the

process

scraping

three-

dimensional models of buildings between 70 to 120 meters of height from OSM (Open Street Map). This resulted in 4,956 high-rise buildings formatted as three-dimensional OBJ models. In order to adapt the 3D representations for wellestablished

computer

convolutional

neural

vision

networks,

techniques we

opted

and for

“tomographic representation” of the buildings. Each building is sliced horizontally using the 3m standard Figure 3: An Academy of Spatial Agents: one of the layouts generated in the simulation

4,596 Highrise Building 3D models

floor-to-floor height adopted in OSM, which results in sixteen figure-ground diagrams of 256x256 pixels.

Data, Shape = [4596, 256, 256] A Building Tensor = [16, 256, 256] Figure 4: Deeprise: dataset

Academic Figure 5: Deeprise: high-rise building design using interpolation

76 | Generative Design

We organized the slices into three groups based on the

the input and generates the slices of a building as its

range of their relative heights (i.e. 0-33%, 33-66%, 60-

output. The discriminator is trained to distinguish the

100%) and sampled them to represent the three parts of

buildings that are retrieved from the dataset from the

the building: podium, midsection, and spire.

ones artificially created by the generator.

We trained a specific Generative Model (IntroVAE) that

After training, the designer can interact with the

merges the qualities of variational autoencoders and

generator, exploring the latent space as the design input

Generative Adversarial Networks (GANs). This model is

(like the input parameters of a parametric model). By

composed of two parts: a generator and a discriminator,

assigning different values to this vector, the generator will

which are trained jointly using a game-theoretic

synthesize new buildings with consistent morphological

approach. The generator learns how to synthesize

features. It is also possible to use the position of different

buildings that seem to belong to our dataset. It uses a

buildings in the latent space to explore interpolations or

compressed representation called a latent vector as

hybrids using basic vector algebra.

Figure 6: Deeprise: design example

novel 3D Deep Learning models and to other building types.

in real-time with trained spatial agents to explore architectural configurations. Deeprise takes the use of precedents to an extreme where the design of new building forms is informed by features learned from

Future of co-creation with AI Design is a communication-intensive and collaborative activity that involves many aspects of creativity, space, and human interaction. The projects above position technology not as a goal or curiosity but as a platform for human-machine collaborations that can target some of

Academic

In the future, we intend to extend this investigation to

thousands of existing high-rise buildings. Between interactive generation and precedent-analysis, both projects address the potential of Deep Learning as a method to infer Generative Models and build workflows for design exploration and co-creation with AI.

these aspects of design. An Academy of Spatial Agents investigates a scenario where designers can interact

Pedro Veloso, one of the founding members of CRAIDL, is a computational designer, architect, and educator interested in the integration of design with ideas from cybernetics and Artificial Intelligence. As a PhD-CD candidate at Carnegie Mellon University, he is developing intelligent and interactive agents for architectural composition using Reinforcement Learning. His current teaching and research interests concern generative strategies for creative and sustainable practices, with a particular focus on models that rely on data and experience. Pedro Veloso

Jinmo Rhee, CRAIDL founding member, a PhD-CD student, a graduate instructor, and studio tutor at Carnegie Mellon University, applies Artificial Intelligence to architectural and urban design, combining his background as a computational designer and architect. Currently, he is studying and researching architectural typology and urban morphology using generative systems and Artificial Intelligence models to discover complex and latent features of forms according to their physical and social context.

Jinmo Rhee

Company

Generative Design with Hypar By Anthony Hauck, Andrew Heumann, and Tyler Goss

Since automated computation became practical more than 50 years ago, professional expertise has become increasingly automated. Codified standards, regulatory frameworks, and statistical analyses have led to services such as WebMD and RocketLawyer, respectively providing common medical and legal advice once confined to human interactions. Neither the medical nor legal professions have vanished, but now many people who had little access to such professional expertise can proceed with more confidence in automated professional expertise superior to previously available advice.

However, with exceptions mostly occurring in academia, until recently software used in the building industry has largely focused on supporting manual accounting (tracking the source and responsibilities of Requests for Information, Field Bulletins, and the like) and the otherwise manual production of specifications and construction drawing packages. By investing in software to support conventional instruments of service, the software industry distracted building professionals from the work in the 1960s and 1970s that focused on automating architectural expertise to produce viable solutions. The revival of this work in recent years has led to practices commonly referred to as "generative design".

76 | Generative Design

In an effort to bring some rigor to discussions of

two from Hypar and Marco Juliani of CallisonRTKL

generative design and its application to building practice,

independently contributed "Functions" to the Hypar

in April 2018 Hypar offered a short article defining

Explore web environment at widely different times,

the term independently of supporting technologies

relying on a common open-source and extensible library

and techniques: "Generative design is the automated

of digital building "Elements" for compatibility. When

algorithmic combination of goals and constraints to

combined into a Hypar "Workflow" in Explore the resulting

reveal solutions." Lately the company has increasingly

functions in combination produce alternate dispositions

focused on the "constraints" aspect of the definition

of program requirements and their comprising units.

as not only lending a necessary determinism to some

The functions comprising the Workflow permit varying

algorithms (i.e., we're building a stadium, not a hospital)

degrees of designer constraint, from relatively freeform

but also as a key avenue for designer participation in

drawing of site boundary and placement of major building

crafting solutions.

masses to setting a number of constraints on the creation of results. By further mapping selected inputs across

The built environment is a collaborative artifact

numerical ranges, each function produces multiple

embodying expertise from many sources. Procedural and

options in parallel, supporting an extensive exploration

artificial building intelligence is now a practical addition to the orchestration producing buildings. Taking as our model the typical collaborative relationship between multiple teams to produce a building, Hypar supports

Scan to See the video for Procedural massing on HYPAR

similar collaborative contributions from multiple sources to produce results. In this first example, three authors,

Figure 1. Procedural Massing A

Figure 2. Procedural Massing B

subdivided and split, and programs assigned to specific zones. Spatial assignments operate as constraints on

design, and construction methods.

subsequent generative procedures, acknowledging

combinatory

possibilities

with

Company

accompanying

statistics to support decisions concerning budgeting,

and preserving the building professional's intervention In a second example, a different set of combined

within the context of multiple options.

functions supports office test fit design and exploration.

Selected or generated zones are then processed in

Office test fits help evaluate the suitability of a building

parallel by a series of space layout routines, each

or floor for a tenant's office needs, essential to proposing

responsible for creating realistic furniture arrangements

and negotiating a lease. Developing a test fit can be

for each space derived from an ingested catalog of

a laborious process with a slow turnaround, requiring

furniture manufacturer offerings. Each program type

several iterations of changes and redesign work.

references a catalog of known spatial arrangements to

Developers and potential tenants of commercial space

suit spaces of different sizes and shapes, which each

may have trouble visualizing the experiential effects of

layout function adapts to the spaces generated by the

design and construction choices when reviewing a two-

zone planner. Resulting layouts can be readily exported

dimensional plan.

to other environments for elaboration into detailed documentation or shared via the web as a basis for

Hypar's office test fit planner makes it possible to produce a test fit in minutes, rather than days, through an "augmented design" interface that combines fast digital sketching with fluid layout automation. The resulting

Scan to See the video for Office Design test fits on HYPAR

space configuration appears as both as a schematic diagram and a detailed three-dimensional model, as well as quantifications which may influence final design, construction, and scheduling decisions. In this Hypar "Workflow", a high-level interactive "zone planner" allows the building professional to quickly generate a space distribution, with circulation routes and program zones created in seconds according to common schemes of spatial subdivision derived from typical horizontal circulation and exiting requirements. The automatically generated zones can be adjusted through direct user manipulation: corridors can be dragged, zones Figure 3. Office Design

76 | Generative Design

further dialog and decisions concerning the design and

•To model accurate structures, Hypar needed to deliver

construction of the office interior.

standard Japanese profiles for hollow steel columns and

In a final example, Obayashi Construction, a strategic

I-section beams. We encoded these profiles into the

investor in Hypar, approached the team with an

platform, making them available not only to Obayashi

interesting challenge to generate commercially and

but to any future workflow by any Hypar user.

legally viable commercial tower designs on sites in Tokyo. Given a site and a limited set of input criteria,

•To accurately place core elements like elevators,

could Hypar not only find a solution but use its generative

bathrooms, and egress stairs, we needed to understand

design capabilities to find the best solution for that site?

not only the common rules and obligatory regulations that govern their sizes but the how they interact with

Our process consulting into such scenarios is straight-

each other and with the rentable areas of the building.

forward. First, we tested the platform's current

Relying on both supplied references and designer

capabilities against the client's problem. After opening

expertise, we created generative functions that solve

Hypar and crafting a workflow, we had a working

this multivariable optimization problem for the building

prototype that could generate steel structure, floors,

professional, who supplies the gross positioning of the

and rudimentary curtain wall and service core systems

building's service core within its construction envelope

within a few minutes.

as an initial constraint.

Next, we needed to identify the limits of the current

In composing and abstracting this regional expertise,

platform. We quickly realized that given an internal

we discovered that by encoding additional building

team of American architects and engineers and current

intelligence

customers largely drawn from North America and

structural grids, levels, and other project measurements,

Europe, the expertise embedded in the existing Hypar

we could facilitate deeper analysis of the building and

function library was largely oriented to North America and

generate much more realistic and accurate designs. The

European standards. The team undertook development

result was a function that creates volumes referred to in

efforts to rectify the lack of captured expertise in

context as "bays" by placing vertices at each grid/level

Japanese building regulations and requirements:

intersection to locate relevant building components at

within

such

construction

datums

those intersections. Any object placed within this spatial

•To generate viable commercial towers, Hypar needed

framework inherits a significant amount of data about its

to understand Tokyo's zoning laws, so the team built a

location in the building, its role was in the larger context

function modeling the bulk and massing requirements of

of its system, and its relationship to neighboring objects

the city's various prefectures.

and affected systems.

fraction of the proposals now available to the Obayashi

steps, the effort to encode Obayashi's expertise into new

design and construction teams.

Functions comprising an effective Workflow required nearly three months before the first viable building was

Work to refine and improve the quality of these results

generated.

continues, and we look forward to bringing this approach

Company

Initially a laborious process through readily apparent

to a variety of building professionals and sectors seeking However, leveraging the encoded expertise to review

to leverage scalable computation and automated

multiple viable proposals for an arbitrary site in Tokyo

expertise to expand and enhance their capacity and

now requires less than five minutes of a building

practices to deliver a better built environment.

professional's time, where one or two days might have been previously expended to produce and explore a

Figure 4. Tokyo Midrise Tower

With more than 20 years’ experience in architecture, engineering, construction, and IT, followed by 10 years at Autodesk as the Director of

Company

Co-Founder, COO, Hypar

Product Management for Revit and the Director of Product Strategy for AEC Generative Design, Anthony has always sought to improve building practices through the strategic application of advanced technologies. He has taught and presented on generative design numerous times at Autodesk University, Revit Technology Conferences, BILT conferences, and the 2017 CTBUH conference. As co-founder and COO of Hypar, he seeks to Anthony Hauck

accelerate advancement in AEC by providing a scalable cloud platform for computational AEC tools. Software Engineer, Hypar Andrew is a software developer at Hypar, with a passion for building the next generation of software tools for designers. He has previously worked as an automation researcher at WeWork, and before that as an architectural designer at Woods Bagot and NBBJ architects. He has written more than 20 plug-ins for 3D modeling software like Rhino and Revit, including the popular "Human" and "Human UI" plugins for Grasshopper. Andrew has studied both architecture and computer science, and has lectured and taught seminars at Columbia GSAPP, Yale University, Princeton University,

Andrew Heumann

and the California College of the Arts. Product Manager Tyler is a registered architect with nearly two decades of experience across all phases of building design, construction, and operations. In this time, he has built world-class design and VDC teams, transformed workflows and built innovative technologies for some of the largest builders and owners in the world (including SHoP Architects, Turner Construction, WeWork, and Walt Disney Imagineering), and been a public advocate for the thoughtful integration of technology in the business of shaping our built environment. He lives in Oakland, California with his wife, children, and approximately 17 bicycles.

Tyler Goss

Academic

shaping facades | shaping infrastructure | shaping cities 49

projects@sorba.com

www.sorba.com

Werkstadt Grasbrook ©MVRDV

Interview

The NEXT Steps in Design

An Interview with Sanne van der Burgh and Leo Stuckardt from MVRDV NEXT Sarah Hoogenboom, Tim Schumann and Aditya Soman

NEXT invents and implements computational workflows within the renowned architecture studio MVRDV. With a mix of project-based work and research, MVRDV NEXT develops new applications of rationalisation, automation and experimentation in architecture. In 2019 they designed for an urban design competition a completely new tool to perform an urban participatory process: the Grasbrook Maker. In an exclusive interview with Rumoer, Sanne van der Burgh and Leo Stuckardt give us insights into the workflow at NEXT, the Grasbrook Maker and the future of Generative Design.

76 | Generative Design

Rumoer: Can you talk a bit about why and how MVRDV

everything, we'd be incredibly busy. Fortunately, while

NEXT was established?

we work [on projects], we develop our own knowledge base of specific MVRDV tools and components. The more

Sanne van der Burgh: The [MVRDV] office saw the

we work on these projects, the larger our knowledge

potential of a new way of working and decided to

base becomes. At the same time, we are also a Help

invest in new technologies within the firm. We saw an

Desk, where we educate and train our colleagues. So

opportunity to start a specialist group within the office

not only do we become more knowledgeable and we

that we decided to call NEXT. NEXT is an abbreviation

expand our knowledge base, but during the, let's say,

for New EXperimental Technologies. And Leo and I set

the journey, our colleagues learn more and become more

that up around 4 years ago. Currently we are a group

independent and aware of what's possible and where to

of five people specializing in the development and

find it and how to use it. So, there's actually a constant

implementation of computational workflows within

evolution of not only deepening our knowledge, but also

the office. We gradually grew into a fully established,

of our colleagues, strengthening their core of what's

specialized unit where data and design are closely linked

possible.

together. And these methodologies are actually at the roots of MVRDV, where we strive to make data driven

Leo Stuckardt: When we started NEXT, the ability

design.

to script was still quite an uncommon skill amongst architects. Of course, we see that this is increasingly

Leo Stuckardt: To add to that, all five of us at MVRDV

becoming a standard part of an architect’s education

NEXT have a background as architects and share a

and most of the young staff at MVRDV is to some extend

fascination for computational design in architecture. But

familiar with computational design. Because of this, the

the second reason why MVRDV NEXT was established

NEXT team supports projects mainly with more specific

is that we acknowledged the rapid shifts within the

or complex computational design questions and design

A.E.C. industry towards digital tooling, global real-time

tasks. In addition, we also do standalone research

collaboration and performance evaluation.

projects that develop these tools further and develop our own libraries of computational tools for future projects.

Rumoer: Is computational design strictly coming from the NEXT group or are there additional departments that

Rumoer: To what extent is generative design used in

utilize computational design? How do you deal with big

projects and in what stage of the design process?

projects? Leo Stuckardt: Generative design is a very broad

Sanne van der Burgh: MVRDV is a company of almost

term, so I think in some form or another we use it in all

two hundred and eighty people, so if NEXT would do

stages, from concept design to execution and also

kind of Software 2.0 and particularly machine learning

Generally speaking, generative design is more suitable

within the [architecture] practice. Yet so far, we have

to some tasks than to others and financial relevance is of

mostly only experimented with A.I. through computer

course also an important criterium, since it justifies the

vision networks for object detection in satellite and street

development of these tools in the first place. So, I think

view data. We have also played a bit with generative

that one of the applications with which we started are

adversarial networks (GAN’s).

facades and building envelopes. This is because on the

I think the question is, can [AI and ML] really enable new

one hand, it's a very repetitive task to do manually and

kinds of architecture? Can it help us to make different

because the conditions for a generative design approach

forms of design rather than only optimizing or speeding

can be framed very clearly; square meter coverage,

up processes? As of now we see already that Adobe or

transparency and impact on structural integrity. This is

Autodesk are implementing neural networks within their

probably the one aspect of buildings for which we have

off-the-shelf software solutions. Once it overcomes

been able to develop complete workflows from concept

this early adopter stage of cloning code from GitHub and

to execution. We also collaborate with structural

hacking things together, these tools will be implemented

engineers and with urban planners for capacity studies

very quickly on a practical level. But if we as architects

and initial FAR density studies, where generative design

want to imagine different applications than Autodesk

can be very useful.

or Adobe, we need a certain literacy of these kind of

Interview

on all scales, from interior design to masterplanning.

technologies. Sanne van der Burgh: We also sometimes see that our colleagues generate designs, solutions, or approaches

Rumoer: Refering to that: how will the role of architects

that need a certain optimization or rationalization in

change due to the development of automation?

order to be physically buildable or constructed. [In these situations], the generative part is created by our

Leo Stuckardt: I think that [the role of an architect] will

colleagues but we develop an approach towards making

change and any guess that we take now will probably

it buildable.

be a wrong one. In my opinion a desirable direction would be that it augments the human designer and

Rumoer: In this issue we focus on Artificial Intelligence

enables collaboration between human and machine.

and Machine Learning applications. Do you think that

I would imagine a form of machine learning that, for

Artificial Intelligence and Machine Learning will change

example, allows you to simply sketch on an iPad and

architecture and is NEXT working in this field?

then based on that sketch neural networks would predict FAR, ecological footprint, technical detailing or other

Leo Stuckardt: We follow these developments closely.

quantifiable design impact. This would actually allow us

I think it's a very exciting shift that's happening with this

to go back towards this very intuitive level of designing

76 | Generative Design

while having machine-counterparts that do predictions

Rumoer: A general question that most of the students will

and estimations based on these drawings. So maybe an

definitely be interested in is: What software programs

attractive direction for things to go towards would be

do you think are the most essential skill set to develop

that we, [the architects], would need to know a little bit

architecture in the future?

less code and sketch a little bit more. Leo Stuckardt: We at MVRDV are all still in love with Sanne van der Burgh: Now, I think that we can definitely

Rhino and Grasshopper. With the latest developments

anticipate that there will be a shift in the profession and

of Rhino.Inside, it can basically be embedded within any

we will definitely not be doing what we're doing now 10

other software. Especially the ability to use Grasshopper

years from now. It is also our responsibility to anticipate

within Revit is really exciting. We are currently testing to

that [change] and to also philosophize about what

switch from Dynamo to Grasshopper. Another interesting

directions it could take and what that would mean for us.

aspect of Grasshopper is its open-source community. If you go on the McNeal forums, you can see how architects

Leo Stuckardt: In addition to that, probably one issue

and designers share plugins and scripts. I think this really

that we are already facing with traditional algorithms

makes it more than a tool and provides a platform, where

within the discourse of architecture is transparency. As

people generously exchange knowledge. In addition to

a user of computational tools, you usually only see input

that, I personally think Python is a wonderful and useful

and output of an algorithm, while a lot of decision making

programming language to start with and probably a good

is actually already embedded within the algorithm itself.

skill that can be used pretty much in any CAD software,

These kinds of issues only increase with the rise of

from Rhino/Grasshopperto G.I.S., Blender and so on.

neural networks. There this entire discussion of black

We also have people writing components in C#, mostly

boxes became a lot more urgent because no one actually

just because the implementation in Grasshopper works

really knows what their decisions are based on and

better and it performs faster. But more important than

there are already many known cases of cultural biases

a particular software skill is probably a general curiosity

that are inherent in these technologies. For instance,

towards what's new and the ability to adopt these

the infamous computer vision networks that detect

things quickly. So some kind of flexibility in thinking is

white, male faces much better than others. In-depth

needed, because in the end you can learn a programming

knowledge amongst designers is needed to recognize

language quite quickly and once you know one it is fairly

these kinds of risks and implicit injustices. So of course

easy to transfer the concepts to another language.

there is a literacy required on a technical level of how

Lastly I would say it's really about connecting different

these mechanisms work and at architects have to be

software and different media to create exactly what

involved in the design of these systems.

you need. As designers we still see an algorithm mostly related to a visual output. We work a lot with video

Can we engage people through gamification and real-

and so on.

time visualization and and give an understanding of the complexities of large-scale planning processes? [In the

Interview

editing, we look into augmented reality, virtual reality

Rumoer: A project of yours caught our attention regarding generative design and application of it in a complex social environment: The GrasbrookMaker- an urban game that combines the interests and wishes of different stakeholders in an urban masterplan. What was the intention and inspiration behind creating this? Leo Stuckardt: GrasbrookMaker was a part of MVRDV’s proposal for an invited competition for a masterplan in Hamburg [Germany]. Preceding the competition

©MVRDV

was a two-year participatory design process, in which

Figure 2: With the mouse, participants can create their prefered urban layout

organizers of the competition tried to understand the desires, requirements, and wishes of local communities. Our intention was to experiment and develop this participatory process further and interweave it with the actual design and realization of our proposal. We wanted to see if participants could become a more active part of the design process through software and design.

end the idea was that] it can become a masterplan that adapts and changes over time as we learn new things [from community members] while building this large part of the city. Rumoer: What are the main parameters in the program and how do you achieve a final score? Leo Stuckardt: The GrasbrookMaker was developed mainly in Grasshopper and Rhino. The parameters that we included were mostly environmental and, in a way, followed a classic urban design approach. Mapping noise, access to green, mobility and transportation, existing urban densities, daylight exposure, and so on. The GrasbrookMaker then combines all these parameters

©MVRDV Figure 1: The GrasbrookMaker allows access for Designers, Stakeholders or Developers

into heatmaps, which indicate better and worse locations for urban programs within the masterplan. Hereby it is important to note that the weighting of these parameters

76 | Generative Design

differs between programs. So, the GrasbrookMaker

residential

generates multiple heatmaps for residential, mixed-

incubators and so on. We then defined weights for each

buildings,

mixed-use

accelerators,

use or commercial programs. In addition to all these

of these so that the GrasbrookMaker could figure out

parameters, we added what we called ‘Activators’.

preferable configurations of the typologies on site.

Activators are special public programs that can be

But of course, the GrasbrookMaker is not supposed

positioned in a dialogue between architect, urbanist, city

to be used only within MVRDV’s office. The proposal

and the public and would also impact these heatmaps.

envisioned this tool to become an integral part of an

The computational approach is based on a global grid

ongoing, participatory process, in which a user can test

across the entire site. Each point within that grid stores

different scenarios by placing activators or modifying

a value for each of these parameters. Those values can

the weightings for different typologies.

then be multiplied with variable weightings to generate heatmaps. If for example access to green is very

Rumoer: How does the interaction and participation for

important and noise is not important at all for a program,

local people work?

you can calculate a score for each cell within that grid and generate a heatmap for good and bad locations for

Leo Stuckardt: Locals would make an account and create

that program. What is important to understand is that the

scenarios on a web platform. They can place activators,

weighting is unique for each program of the masterplan.

design parks, and draw public spaces. Developers would

MVRDV’s competition team designed these public

interact with this platform by defining development

activators following the competition brief and defined

requirements, For example if a developer is planning

target densities and requirements for office buildings,

to build new office spaces he could prioritize access to public infrastructure and high visibility”. Maybe another one says “I want to build residential buildings, and access to water and low noise is important for that.” This information is then used to create development profiles. As people place public infrastructureand other public facilities within the platform the GrasbrookMaker will generate design scenarios by combining the wishes of developers and local people. The outcome is a growing number of scenarios for the whole site. So we would get large numbers of masterplans. How will these scenarios

©MVRDV Figure 3: A modular system allows the design of complex urban structures

then be negotiated? How can you overlay and compare different scenarios? This is where probably some sort of machine learning could be useful. But it's also

the design proposals but the crucial differences between

of the competition entry.

the proposals were obviously around other questions. We wanted to engage with communities on a deeper

Rumoer: How does negotiation happen?

Interview

something that was only sketched out within the scope

level and communicate the actual seriousness of urban planning. That's why we called it a serious game.

Leo Stuckardt: Yeah, I think negotiation and prioritization would remain a crucial task for the experts – architects,

Rumoer: At the presentation of the GrasbrookMaker, the

planners and policy makers. You could overlay these

reactions of the audience were mixed. Is there a general

scenarios and for example try to identify majority votes

skepticism and lack of acceptance of thinking about

– re-occurring features within multiple scenarios. We

urban design in a gamification manner?

also wanted to encourage ways to communicate the considerations of architects and urbanists to a general

Leo Stuckardt: Indeed, there was some skepticism

public. In my opinion there is a lot more work to be done

particularly towards what was implied with regards to

on that front. One of the main outcomes of this two-year

traditional German planning procedures. I would like to

participatory process was, for instance, that residents

stress though that the proposal by MVRDV was not only a

specifically wished for a pharmacy on site. While this

flexible software and a game, but also a fully developed

should be taken seriously, we believe that if you find

urban plan by our urban design team. This plan had a

other ways to engage with people, they might be able

similar level of detail to the other proposals and covered

to think about these large-scale developments in more

all requirements of the brief. All we did in addition was to

holistic ways. A pharmacy could be placed within any of

explore forms of flexibility in the scheme. The planning

©MVRDV Figure 4: Design variants created in the GrasbrookMaker

©MVRDV Figure 5: Physical model of the urban masterplan

76 | Generative Design

and realisation of such a large area is a long process.

way to face a reality check for these kinds of visions.

Conditions and requirements will likely change within

Germany or Hamburg may not be the place that will

the next 10 or 20 years if you look at the rapidly changing

radically innovate on urban planning methodologies

demographics or the impact of the climate crisis. Is there

but these ideas of gamification, participation and so on

a way that we can make those changes an integral part of

are deeply rooted within MVRDV. For example, Almere

the planning process and retain some form of flexibility

Oosterwold, which is actually under construction right

and resilience? It appeared that especially these ideas of

now, has a similar or maybe even more radical idea of

flexibility in process and design were very challenging to

creating a completely different form of city. We definitely

the city of Hamburg’s conventional planning approach.

take this skepticism as productive feedback, which we

Maybe a second reason why the proposal encountered

are trying to learn from and still believe in the need for

skepticism is that it went beyond the scope of a common

these kinds of proposals.

architectural or urban design brief. It challenged how building policy can be formulated and maybe should be

Rumoer: Will this game be further developed as a

revised or experimented with. We didn’t only encounter

generalised framework that can be adapted to different

scepticism though. There were many people who were

sites and contexts? What improvements or changes will

excited by the proposal and we had a really productive

be made to the game for future utilization?

and interesting conversations with (mostly the younger part) of the audience. For us this was also a very useful

Leo Stuckardt: We are still exploring similar mechanisms, not necessarily by continuing development of a GrasbrookMaker 2.0, but rather by expanding on the idea of heatmaps and generative urban program placement. These mechanisms have already been utilized in other urban projects by MVRDV and are definitely being developed further on the computational or technical side. Most improvements or changes are probably on the narrative side and a focus on quantification of design performance. Another aspect of GrasbrookMaker that we still pursue beyond the specific scope of the design brief, is how to engage with more general questions of designing public building policy. For instance, our project

©MVRDV Figure 6: Almere Oosterworld (https://www.mvrdv.nl/projects/32/ almere-oosterwold)

SolarScape visualises the impact of public daylight regulations on the densification potential in Rotterdam. In this sense, several topics that the GrasbrookMaker

fields of our daily life. Game-engines are developing

in completely different formats.

rapidly and are already taking over the traditional visualisation industry. It's pretty easy nowadays to build

Sanne van der Burgh: I agree with this. At first this might

an interactive application and this is something that will

take unrecognizable forms, but we see more and more

enter the architectural design space as well. We can

often in our work that we develop parts of a framework,

expect that we will less and less model design scenarios

which come back in other projects. So, we are constantly

through static geometry, but that they will increasingly

developing and evolving. But of course, every now and

talk back to us in some form. The other aspect of the

then, things don't work out as planned. And that’s all

GrasbrookMaker that is relevant in our opinion deals

part of the structure of an innovative trajectory. It's a

with the communication and exchange of data between

learning curve, but it's a very enjoyable learning curve.

multiple stakeholders within a single model. This is

Sometimes it's not even the content of the product or

already a reality now amongst planners in B.I.M and

tool you develop, but it's the way you explain it to people

amongst larger audiences in gaming. So, whether it's in

and how you frame for example, ownership's, roles and

the shape of the GrasbrookMaker or takes other forms, I

responsibilities.

am sure that in the coming years we will see this kind of

Interview

tried to address are still relevant and are just resurfacing

participatory, playful ways of immersive design. Leo Stuckardt: There is this kind of awareness within MVRDV that we will keep on developing and proposing

Rumoer: What was the team dynamic between the

a concept, until it gets built at least once. So, I think

MVRDV NEXT group and the more classic architects

somewhere along that line, we will keep on proposing

within the project?

and developing the GrasbrookMaker in some form until it is implemented. We believe in the relevance of these

Leo Stuckardt: I would say we collaborate based on

ideas and there will be a right moment where this will be

mutual respect. But there are key-differences in the

implemented or in some other form.

process that both sides need to be aware of. Anyone who has been working with computational design strategies

Rumoer: The Grasbrook Maker was maybe a bit ahead

is probably aware of the development stages of a script

of its time in terms of general public acceptance. Do

and how they might appear non-linear in comparison

you think, in the next 5-10 years we will be able to build

to a more traditional design process. The process of

architecture and urban spaces with this type of active

a drawing for instance, appears in most cases quite

and open digital participation?

linear – meaning that half-way through the process you have completed half of the drawing. When developing

Leo Stuckardt: Things are definitely changing in that

a script however you might spend 80% of the time on

direction and gamification in particular is entering most

developing the algorithm for this drawing. Then you run it

better and how to exchange information between us

A design team might get anxious throughout these first

[the NEXT team] and the design team. These feedback

80% of the process. One thing we needed to learn in

sessions are super important to us as this model of

collaboration with architects is to produce presentable

expert teams and design teams is still a learning process

output at any stage in the development of a script. In the

within MVRDV.

Interview

and produce the entire thing within seconds or minutes.

context of the GrasbrookMaker ‘dynamic’ is probably the right word. It was dynamic, turbulent and, I think for everyone, a novel and challengingapproach. But in the end, everyone within MVRDV was very happy and proud of the project, even though we didn’t win the competition. Rumoer: I imagine there is also a learning curve on how to improve the interaction between the designers and the NEXT team? Leo Stuckardt: Absolutely. We try to do that after every project. At the end of each project we have a de-briefing session where we evaluate the collaboration between us

Visit MVRDV NEXT here:

and the design teams to understand what could be done

https://www.mvrdv.nl/themes/15/next

Sanne van der Burgh studied Architecture at TU Delft and worked at the Chair of Design Informatics. She joined MVRDV 12 years ago, started NEXT within the company, and is now a Senior Associate. Leo Stuckardt studied architecture in Berlin and Delft and was part of 'The New Normal' think-tank at Strelka Institute, Moscow. He is co-founder and Project Leader of the MVRDV NEXT team and currently a phd Sanne van der Burgh

Leo Stuckardt

candidate at TU Berlin.

Academic

Are you looking for a new challenge? Join Arup today! The Amsterdam Office is looking for interns and experienced professionals in Acoustics and Building Physics. Check out our vacancies at jobs.arup.com, apply directly or send your motivation and resume to recruitmentnl@arup.com.

Project: Elements, Amsterdam (2020), Koschuch Architects

Analysis of a Two Storey Complex house

Graduate

Topology Optimization: Architectural lessons from Topology Optimization Ir. Rick van Dijk, TU Delft, Architecture and the Built Environment

Building with earthy materials requires new methods to generate architectural geometry and possibly buildings. This research implements Topology Optimization into architectural models, in order to find geometry based on supports, forces and voids. This implementation can only be made by adding density-dependent forces, which are important in architectural models. Self weight, snow loads and roofing constraints are added in order to make more reliable calculations. To generate architectural geometry, the methodology is translated to 3D geometry and several design problems are tested. The results show domes and arches being generated, and believable, strong geometries. Insights from these design problems show that Topology Optimization can be used to generate geometries for masonry buildings.

76 | Generative Design

Introduction Topology Optimization is a mathematical approach in

In optimization problems, the system is always

designing geometry, where the volume is minimized,

minimized, so instead of maximizing the stiffness, the

while still reaching a high stiffness. It is often used in

compliance is minimized. Compliance is a value of how

mechanical and aerospace engineering to optimize

much the whole system can move and is written as in

parts so they require less material. The main idea behind

figure 1. To find the optimal solution, Gradient Descent

Topology Optimization is to calculate what voxels (or

is used, which will set values of each element based on

pixels) in an element are important for the stiffness

its derivative of the compliance.

and what voxels can be removed. Because there is no preconceived shape, Topology Optimization can create

Liu and Tovar (2014) made changes to this standard

innovative and high-performance shapes.

method of Topology Optimization to make it work in 3D. The structure of the algorithm and main calculations

Standard Topology Optimization

are identical, but Liu and Tovar added a new Stiffness

The method (originally developed by Bendsøe and

matrix for the FEA and used a strict numbering system.

Sigmund, 2004) starts with dividing the design space in

Each element now has 8 nodes and each node can move

pixels and preparing the supports and loads, the steps

in 3 directions, which causes the matrices to increase

can also be seen in figure 1. Each pixel has 4 nodes that

by a power of 3. Furthermore, loads and supports can

can move in both X and Y directions (their Degrees of

be set in the same way and this can already be used in

Freedom). Supports are defined as nodes that cannot

some models. Figure 2 shows a simplified version of the

move in one or all of the directions. With the supports

QNCC building, where Topology Optimization is used to

and forces defined, the displacements of the nodes can

generate columns for the big roof.

be calculated using Finite Element Analysis (FEA).

Figure 1: Example of the steps in Topology Optimization (Bendsøe & Sigmund, 2004)

Graduate Figure 2: Geometry generated from Liu and Tovar's (2004) algorithm

Topology Optimization in Architecture Architectural cases are different than cases in mechanical

dependent on the density, the force should be included in

engineering, as most forces are not predefined, but are

the derivative. Rewriting the derivative previously found,

dependent on the density. The geometry itself is heavier

but without the derivative of the force being 0, gives a

than the forces on the system. Placing a voxel will then not

new derivative, as shown in figure 3. When looking at

always improve the stiffness, as it can also generate new

this formula, one can see that the result of the derivative

forces which decrease the stiffness. Density-dependent

is no longer always negative. The graph of an element

forces have to be added to the algorithm, which are new

is no long monotonic and therefore Gradient Descent

forces on nodes where the element around the nodes

can no longer be used. Therefore, another optimizer is

exists. This can be mathematically described using a

implemented, called the Method of Moving Asymptotes.

sigmoid function (S(x)), which sets values to either 0 or 1, depending on x. Previously the force was pre-set and constant, so the derivative of the force was 0. Now that the force is

e dFe dKe dC p−1 = −pxe (2UeT −UeT Ue) dxe dxe dxe Figure 3: Density-dependent compliance (Langelaar, 2020)

76 | Generative Design

Solving the system now still relies on a preset force, which

snow load on the roof. This is a force that is placed on

is usually not the case in architectural problems. Instead

the highest element, which should make sure the roof

of forces in the system, the constraint in architectural

will not fail when forces are placed on it. In other words;

cases is that each void has to have a roof over itself. Or

the element will gain a force if the sum of the elements

in other words; for each void, the sum of the elements

above it, including itself, is equal to 1. This can be

in the column (above the void) should be larger than 1.

mathematically described using a smooth-Heaviside

Figure 4 shows the roof-constraint working, for columns

function (SH(x)), which sets the value of y to 1 if the

where there is no roof, the algorithm sets all the values

value of x is in the range of 0.5 and 1.5.

above the void to a certain value. Note that “grey” values are punished, in order to get black and white results.

The total forces can now be written as the sum of a preset

Another sigmoid function is implemented so that values

force, the self-weight and the snow load. Summarized,

that are grey are considered as a 0 and values close to 1

the force on an element can be written as shown in figure

are considered as a 1. After many iterations, the shape is

black and white and shows to be a dynamically relaxed structure. However, it is very thin. The optimizer will minimize the volume and increasing the thickness will generate more forces.

Fe,total = Fpreset + S(xep · Fself weight) + Fsnow if k ∈ K : SH( columnxe xk,i) · Fsnow Fsnow = if k ∈ /K:0

When this would be built, it can be quickly seen that any forces on the roof will make it collapse. Another type of density-dependent force has to be added, namely a

Figure 4: Results of the roof-constraint

Mathematical description of an element's forces (Langelaar, 2020)

In order to validate these findings, several tests were

the number of voxels. Doubling the resolution of a 3D

performed, comparing the algorithm to the Topology

problem will increase the Degrees of Freedoms by a

Optimization software in Ansys. The results were

factor of 8. The most time the algorithm takes is spent

comparable for simple topology optimization problems,

solving the system and calculating the displacements.

but no roof-constraint could be added in the software.

The result of the toy-problem shows the generation

It requires further research to find the feasibility of the

of dome-like structures above the large void and the

generated geometry. However, the results of certain

beginning of arc-like shapes above the doors. Domes

configurations can be analyzed and compared with

and arches are very common in masonry structures, as

existing architecture.

they allow for building materials with high compression,

Graduate

Results

but low tension quality. These results show that the During this research, toy-problems were used to solve

algorithm generates geometry that is representing

each step. Figure 6 shows the result of 2 configurations

some elements in architecture. However, it can be

of the final toy-problem when all the constraints were

noticed that the dome has many inaccuracies, due to

added. It shows the roof-constraint being added to

the resolution and specifics in the optimization process.

columns where voids exist and this causes a roof to

Another conclusion that can be drawn is that cubic voids

be created. The main problem in this configuration is

(currently the only possibility in the algorithm) are a poor choice to use, as they don’t allow for optimal geometry.

Figure 6: Results of the toy-problem

76 | Generative Design

Figure 7: Haus am Horn, generated with Topology Optimization

Lastly, the question is “Can we generate buildings

time, but still, some shapes can be seen. Walls in

using Topology Optimization?”. To reflect on this

between rooms are always built, as they are needed to

research, an example of a Bauhaus building is taken as

carry the roofs. However, above all the doors, arches

a configuration and its geometry can be seen in figure

are generated to save material above them. The rooms

7. The configuration of the Haus am Horn is used as

themselves all are generated with domes above them,

input, together with the roof-constraint and self-weight.

with the large room having a high dome. The section of

The results are still quite poorly because of calculation

the dome is promising and hallways are starting to be optimized, however being subject to the low resolution.

[1]Bendsøe, M. P., & Sigmund, O. (2004). Topology

Optimization could be a method to generate buildings.

Optimization. In Topology Optimization. Springer Berlin

The process itself is directly linked to FEA and even

Heidelberg.

more constraints can be added in the optimizer itself.

https://doi.org/10.1007/978-3-662-05086-6

Graduate

References To answer the question; yes, I think eventually Topology

However, more research is needed to generate more useful geometry, that eventually could also be tested

[2]Langelaar, M. (2020) (Personal communication,

in structural calculations. Essential for this useful

June 22th 2020)

geometry is the resolution of the voxels, which allows for more accurate results. One other constraint that could be

[3]Liu, K., & Tovar, A. (2014). An efficient 3D topology

added are the voids themselves; instead of starting with

optimization code written in Matlab. Structural and

cubic voids, starting with a 2D layout could be better,

Multidisciplinary Optimzation, 50(6), 1175-1196.

where the optimizer is allowed to generate optimal

https://doi.org/10.1007/s00158-014-1107-x

voids as well. Concluding, Topology Optimization could generate and shape masonry architecture, but a higher resolution and more optimal configurations are needed.

From a young age, Rick knew he wanted to be an architect, but during his studies, he grew passionate about programming and game design. Thus the interest in computational design was born, resulting in a portfolio that always combines architecture with math and code. Rick recently graduated from Building Technology with a Cum Laude degree and since has been working at a large engineering office, writing software to optimize several workflows.

Ir. Rick van Dijk

Digital Blue Foam, urban plan

Company

Personalized Generative Design Generative design is yesterday's news. Are you ready for what comes next? Cesar Cheng and Sayjel Vijay Patel, Digital Blue Foam

“Generative design” - the iterative process of using algorithms to produce a number of outputs based on design constraints [1] - is being championed by architecture software giants as “the future of making.”[2] But the power to instantly create thousands of options is already yesterday’s news. As makers of software for architects, we see young designers all over the world using tools every day to automate design choices and options. While generative tools such as visual programming and scripting languages are proliferating rapidly, some familiar problems and challenges remain:

1. Generative design only works on narrowly defined design problems. 2. It is difficult to share and reuse algorithms. 3. The abstraction of design problems into an algorithm is an ‘alien way’ of thinking for many designers. [3]

This essay considers several advances in AI applications that may help to address these challenges.

76 | Generative Design

Augmented Intelligence

Learning from AI Applications

Today we can envision a future where generative tools

In personalized learning [5], the instructional approach,

like scripting, machine learning, and AI are used to

learning objectives, content, and pace are dynamically

supplement and support human intelligence as opposed

adapted to the needs of each learner. The activities and

to replacing it. The concept of augmented intelligence

resources offered are customized for the unique needs

conveys how humans and machines will co-exist, co-

of each individual student. It is now possible, via data

operate and co-create in a mutually beneficial fashion.

science and AI technologies, to gauge the student’s

[4] This reveals a new range of possibilities for human/

learning style as well as their degree of knowledge on

machine collaboration.

a given subject automatically, and use this profile to deliver customized support and instruction, making

In our work at Digital Blue Foam, we are interested in

personalized learning a more meaningful experience for

facilitating the creative dialogue between designers

both instructors and students. In this case a feedback

and computers. Rather than look at generative tools

loop is developed between the AI system, the student,

like scripting or

and the instructor, which enables each to enhance their

machine learning models as task-

automating black boxes, we are inspired by how AI

abilities to learn and instruct.

is used in other non-design endeavors to facilitate “natural” interaction between humans and computers.

Another example of AI personalization can be found in

We will look at two specific examples - “personalized

chatbot applications. The advances in Natural Language

learning” and “personalized chatbots” - and what they

Processing (NPL) that made it possible for personal

might mean for architectural design tools.

assistants such as Amazon's Alexa or Apple's Siri to respond to human language inputs are now widely used

Figure 1: Augmented Intelligence is the hybridization of Human and Machine Intelligence

Figure 2: Replika. https://www.all-turtles.com/case-studies/replika

Company Figure 3: Digital Blue Foam, Sketch tool for building generation

in commercial customer service applications. Other

What does an NUI look like to an architect?

interesting and creative use-cases for chatbots are,

For design professionals working with AI assisted tools,

however, also becoming popular. Replika, a hybridized

design exploration should not be limited to simply

diary/personal assistant/social companion, uses an

defining parameters and letting a generative solver

Artificial Neural Network(ANN) to mimic the user's

provide a number of solutions to meet them. Instead,

individual speech and writing patterns. It asks questions

we propose to construct a design dialogue between the

about the user, and eventually, as interactions stack up,

designer and the AI assistant, one in which designers

it learns and develops its own character in a way that

are able to develop, modify, and evaluate their design

reflects that of the user.

decisions as the dialogue unfolds. This results in a more productive interaction between the designer and the

Natural User Interface

machine, since at any given point the conversation can

A natural user interface (NUI) is a mode of human-

be stirred in a different direction allowing for a more

computer

actions

flexible use of generative design. Furthermore, as the

related to natural, everyday human behavior.[6] For

designer continues to interact with the AI assistant, the

chatbots and teaching tools, conversational AI mimics

AI begins to identify design patterns, style preferences

human conversation patterns to create a seamless

and particularities that are unique to each designer, and

user experience. While some NUIs rely on devices for

this results in a personalized experience for the designer

interaction, more advanced NUIs, such as Alexa, are so

where their unique design abilities are augmented by the

unobtrusive that they quickly seem invisible.

computing power of the machine.

interaction

that

uses

intuitive

76 | Generative Design

Figure 4: DBF, design solutions generated using AI personas

At Digital Blue Foam, we have built a platform that

Foam’s sketch tool feature. First the designer states

provides users with a design interface to collaborate

the overall goals and constraints of the project such as

with an AI design assistant. The designer is not limited

GFA, maximum height, lot coverage, and so on. These

to selecting rigid input parameters, but instead they

will be used to run calculations in the background. Then

can sketch and prototype ideas, similar to the way

the designer initiates a dialogue with the AI assistant by

traditionally

quickly

sketching some strokes to subdivide the working plot.

draw a few strokes on a napkin or build a study model

creative

professionals

would

Once provided with this information, the AI assistant

by stacking and recombining foam block pieces. The

begins to generate design options, which can then

sketch strokes are used to trigger a dialogue with the AI

be evaluated and modified in relation to the goals of

assistant, which in turn enhances the design outcome

the project. As the design solutions are generated

by learning from either existing data-sets or from the

and presented to the designer, they can change their

designer’s own choices, and contributes to the dialogue

mind about the initial sketch strokes and create a new

by suggesting better-performing solutions.

sketch that will initiate a different response from the AI assistant. In this way the design dialogue is kept alive

To illustrate this, in figure 3, we present a sequence

and continues to evolve through the interaction between

of interactions that are possible using Digital Blue

the designer and the machine.

Current generative design tools have fundamental

advantage of the troves of data and limitless computing

limitations that can be overcome through a more

power available online to drive sustainable output.

natural approach to design-computer collaboration. By adopting advances in other applications, such as

To address this, we at Digital Blue Foam use augmented

chatbots and personalized learning, we can use AI to

intelligence — the sensitivity of designer intuition,

facilitate a seamless creative dialogue between designer

multiplied by the power of machine intelligence — to

and computer, paving the way for future design tools

step up the productivity of design workflows. Ultimately,

that evolve and react to the tendencies and cognition

our hope is to redesign the way architects and planners

patterns of each user.

imagine spaces for current and future generations to

Company

Conclusion

live, work, and play. About Digital Blue Foam At Digital Blue Foam, we develop AI-powered solutions

Our team consists of architects who love leveraging

to steer a desperately needed revolution in the building

technology and are also software developers, product

industry towards carbon-negative projects. Presently,

designers, machine learning engineers, and user

designers use inefficient tools that do not take full

experience researchers.

Figure 5: Digital Blue Foam, urban plan

[1]https://en.wikipedia.org/wiki/Generative_design

[6]https://whatis.techtarget.com/definition/natural-

[2]https://www.autodesk.com/solutions/generative-

user-interface-NUI#:~:text=A%20natural%20user%20

design

interface%20(NUI,the%20purpose%20and%20user%20

[3]Turkle,

Sherry

Seymour

Papert.

(1991.)

Company

References

requirements.

Epistemological Pluralism and the Revaluation of the Concrete. [4]Zheng et al. (2017.) Hybrid-augmented Intelligence: Collaboration and Cognition. [5]https://www.edglossary.org/personalized-learning/

Sayjel is the CTO and co-founder of Digital Blue Foam, an AEC startup with global customers, developing bespoke web-based tools and operating systems to accelerate the transition to carbon-negative design processes. A MIT-trained architect and computational design researcher, he was a Founding Assistant Professor, at Dubai Institute of Design and Innovation (DIDI), an MIT-affiliated design university pioneering a novel cross-concentration design education. Before that, he was a researcher and designer with the SUTD DManD Center, MIT Digital Structures, MIT Senseable City Lab, and the RMIT Spatial Information in Architecture Lab. From 2013-2018, Sayjel was the founder and coordinator of SUTD and MIT CodeKitchen, where he organized over a hundred peer-to-peer technical workshops on a variety of Sayjel Vijay Patel

topics. Sayjel publishes at top computational design conferences, including ACADIA, Design Modelling Symposium, ECaaDE, and Design Computing and Cognition.

Cesar works as product developer at Digital Blue Foam. He is an architect and urban designer specialized in computational design, urban data analysis and material research. He is a graduate from the EmTech program at the Architectural Association. His work focuses on the digital transformation of the AEC industries with particular interest in computational design, artificial intelligence, spatial data analytics and material research for applications in digital solutions for the built environment. His work has been published at IASS, ASCAAD and the Architectural Science Review. He also taught computational design and digital fabrication workshops in Europe, Asia and America. Prior to joining Digital Blue Foam, Cesar practiced in architecture and Cesar Cheng

urban planning in Boston, New York, London and Panama. 76

LETS DESIGN THE FUTURE TOGETHER

Are you driven to design the environment of tomorrow? Ben jij gemotiveerd om de omgeving van morgen te ontwerpen? Let’s meet.

www.inbo.com

Company

Data-driven design for complex, multi-disciplinary projects Our digital way of working at Royal HaskoningDHV ir. Jeroen de Bruijn, ir. Jamal van Kastel, Royal HaskoningDHV

The building industry deals with increasingly complex design challenges. Measurable performances and close alignment between design disciplines is more important than ever to achieve more sustainable and better-performing designs. A data-driven design approach provides quicker, more cost-effective and optimal design solutions.

Royal HaskoningDHV has embraced a digital way of working with open arms. At Royal HaskoningDHV, we often tackle complex, multi-disciplinary projects, such as hospitals, sports venues, data centres, airports, high-rise buildings and urban development. Such projects require close alignment between multiple disciplines. A data-driven design approach helps streamline the process and makes the impact of design decisions insightful.

With this article we want to illustrate why a data-driven design approach should be (and will become) the new way of working. This article highlights how a computational design approach has contributed to the success of one of Royal HaskoningDHV’s most recent projects; the integrated design of a 20-storey timber high-rise building. Additionally, we briefly illustrate how two emerging technologies – generative design and machine learning - provide solutions for other challenges in building practice.

76 | Generative Design Figure 1: Computational design workflow: various design and engineering modules connected via cloud-based interoperability platform Speckle

1.Monarch IV - integrated design through a digital way

of expertise include structural engineering, building

of working

physics, MEP, sustainability and design integration.

Monarch IV is a timber high-rise building in The Hague

The project started amid the second wave of COVID-19

commissioned by Rijksvastgoedbedrijf (the Central

infections. Real-life meetings for design coordination

Government Real Estate Agency). Once complete, it will

were therefore ill-advised. Instead, Monarch IV is

provide approximately 19,000 m² of much-needed office

developed through a series of online design workshops.

space for government employees in The Hague. Key

Each

requirements included the use of a parametric approach,

HaskoningDHV got together and zoned in on a different

that the building should be constructed with wood and

set of topics, starting with the broad design concept and

meet the sustainability goals of Rijksvastgoedbedrijf,

gradually converging towards the details of the design.

workshop,

Rijksvastgoedbedrijf

and

Royal

and that the project was to be completed in a very

short lead time. Starting point is a conceptual

1.1 Parametric coordination model

design made by Rijksvastgoedbedrijf. Together with

Monarch IV is designed using a computational design

Rijksvastgoedbedrijf Royal HaskoningDHV developed

workflow, developed concurrently to these workshops.

the integrated design of Monarch IV. Relevant fields

The computational design workflow digitally connects

engineering module. The modules are all connected to

when everyone works from their ‘home office’). At

the parametric coordination model using open-source

the core of this workflow is a parametric coordination

interoperability platform Speckle. With Speckle we

model (fig. 1). The building’s geometries and reference

create live connections of geometry and data between

lines are set up parametrically and are controlled by

the various Grasshopper modules via the cloud. A change

various sliders that correspond with the bandwidth of

in the coordination model is automatically transferred to

design possibilities. The coordination model controls

all other modules.

Company

the design processes of the team members (very handy

the interrelationships between key elements such as connection nodes, building levels and floor construction.

1.2 Module one: structural optimisation

During the design workshops, we used the model to

One of the first modules we added to the workflow

support and substantiate the discussion on various

was a Grasshopper script for structural optimisation of

topics. The workshops unveil the most important

the timber diagrid construction. In their initial design,

design parameters of the project. Each workshop, the

Rijksvastgoedbedrijf has already optimised the diagrid

design team determined which functionalities would

by gradually decreasing the profile dimensions on higher

be added to the computational design workflow to best

floors (corresponding to the gradual decrease of total

contribute to the decision-making process (fig. 2).

structural loads). Diagrid dimensions are determined

These functionalities are implemented as engineering

by floor: on each floor the element under highest stress

‘modules’ that run analyses and/or optimise parts of

dictates the minimum profile dimensions of all elements

the designs. The modules all run on different laptops:

on that floor.

each team member was in control of their respective

Using a Karamba model (connected to the coordination

Figure 1: Parametric coordination model built in Grasshopper.

Figure 3: Structural optimisation using Karamba.

76 | Generative Design Figure 4: Solar irradiation analysis using Ladybug

model via the cloud), our structural engineer colleague

of the solar panels in order to establish the maximum

verified the initial optimisation of Rijksvastgoedbedrijf

potential façade area for solar panels. The results of this

and

analysis informed the integration of solar panels in the

minimised

profile

dimensions

accordingly.

However, with the same Karamba model we could also

façade design.

easily optimise the dimensions of each diagrid element individually, as opposed to standardising elements

1.4 Module three: climate analysis and design

per floor (fig. 3). The result: a material reduction of

exploration

approximately 30%.

There is a direct relationship between the structural diagrid and the façade: the profile dimensions of the

1.3 Module two: solar irradiation analysis

façade elements stem from the dimensions of the diagrid

A second module we added to the computational design

structure. Minimising the diagrid elements through

workflow aims to find the optimal façade areas for

structural optimisation not only resulted in a 30%

solar panels. The module uses Grasshopper plug-in

reduction of materials, but it also increased maximum

Ladybug to quantify the solar irradiation on all façade

glazing ratios on all floors.

segments (fig. 4). Graphs make insightful which parts

The optimised structural diagrid showed the maximum

of the façades are in shadow for large portions of the

achievable ratios. However, the thickness of the opaque

year (either because they’re North-facing or because of

façade elements can be increased to decrease glazing

the drop shadow of the Monarch’s neighbouring high-

ratios, which may prove desirable for daylight levels,

rise buildings). The potential solar gains of each façade

thermal comfort and cooling and heating demands

segment are weighed against the energy pay-back time

(amongst others!). These performance criteria are also

the designs, the dashboard reveals the design decision

values and the size and positioning of solar panels.

that leads to the optimal balance between architectural

In preparation of the final design workshops, we

design, daylighting and thermal energy demands.

Company

impacted by other design parameters, such as insulation

explored the impact of these architectural design parameters on façade performances. Each floor and

1.5 Module four: BIM interoperability

each façade orientation have unique design conditions

Another advantage of our data-driven approach: it is

that need to be taken into consideration. Together with

easy to share geometry and data between different

the aforementioned design parameters this results in

software packages. After adding a few parameters in

a matrix of thousands of unique design possibilities.

Grasshopper, we utilise the Speckle platform to instantly

For this final phase of the project, we leveraged

generate the initial BIM model in Revit. In Revit, the BIM

Grasshopper’s ability to rapidly iterate through design

modeller focusses on any specific BIM concerns and

alternatives (fig. 5). A combination of various plug-ins

derives all drawings. At Royal HaskoningDHV, we use

and a few custom components enabled us to generate

Speckle as part of our own interoperability platform in

and evaluate the complete matrix of design possibilities

which we also connect various FEM software packages

fully automatically. After our building physics colleague

and integrate other tooling

had his laptop churn out simulations throughout the weekend, we had a complete data set of 3,600 unique

2 Developing our way of working

design alternatives.

With Monarch IV, we illustrate the benchmark of

We streamed the data set to a data analytics dashboard

our digital way of working at Royal HaskoningDHV.

that shows all design alternatives and their performances

Simultaneously, we’re constantly looking to further

alongside each other (fig. 6). By analysing and filtering

develop our way of working to deliver even better designs. Therefore, various teams are working on the exploration and development of new digital tools which can be applied in practice. We will describe two examples of such developments. An example is the application of evolutionary algorithms for structural optimisation. Our colleagues first built a parametric model of a steel warehouse in Grasshopper with various parameters, including the grid dimensions and the possibility to use beams or different truss types for the roof. The model was then connected to a cost calculation module, which considers the weight, welds, paint etc. Finally, they optimise the structure using the

Figure 5: Automated design generation using Honeybee and Colibri.

76 | Generative Design Figure 6: Interactive design exploration using Design Explorer.

Wallacei plugin, which generates the most cost-efficient

think will be a new standard way of working. At Royal

structure. Without use of evolutionary algorithms,

HaskoningDHV, we embrace a digital way of working

optima can only be reached with brute force calculations

to make our work easier, faster and smarter, delivering

or with a lot of manual work, which both require a lot of

better results for our clients and society.

time and effort. Another new project focuses on machine learning. Currently, it is not possible to accurately predict the site-specific wind loading for any location in The Netherlands. Except, of course, for the 48 KNMI weather station locations across The Netherlands. Our colleagues trained a machine learning model using the KNMI data and terrain roughness data of the Netherlands in order to generate a predicted wind rose for any location in The Netherlands (fig. 7). Conclusion The up-and-coming techniques and design processes highlighted in this article are only the start of what we 84

Figure 7: Evolutionary algorithms for accurate wind load predictions.

Company

ir. Jamal van Kastel is a parametric/computational designer driven by an ambition to bring together architecture and engineering in a performance-driven design approach. Since finishing his master's Building Technology at TU Delft, Jamal has been working at Royal HaskoningDHV. As part of a team of architects and computational designers, he works on a broad range of design projects, ranging from building design to master planning. Here, he leverages computational design methodologies to create more sustainable and otherwise better buildings and environments. ir. Jamal van Kastel

ir. Jeroen de Bruijn is a BIM coordinator and parametric lead who's always looking for ways to improve a process and utilise the power of new digital solutions or develop them if needed. He gets energy from organising the implementation of these new digital solutions and inspire people to apply them. After finishing his master's Building Technology at TU Delft, Jeroen has been working at Royal HaskoningDHV in various roles.

ir. Jeroen de Bruijn

Royal HaskoningDHV has been connecting people for 140 years. Together, through our expertise and passion, we have helped contribute to a better society and improved people’s lives with work underpinned by our sustainable values and goals. Our 6,000 colleagues, spread over 140 countries are committed to our promise to enhance society together. Current vacancies: https://www.royalhaskoningdhv.com/en-gb/careers/international-vacancies https://www.royalhaskoningdhv.com/nl-nl/nederland/werken-bij/vacatures

completely online on the digital platform of ISSUU.

BouT

Board 26 passes the baton...

step towards transitioning BouT periodicals RuMoer The transition has led to increasing readership and RuMoer’s committee has successfully published three periodicals themed Black Swan (74), Urban Grow (75) and Generative Design (76). The relentless support and creative promotional ideas by the public relations and media committee has been instrumental in launching a new Instagram handle for the periodical of RuMoer, that has been successfully made available online from this year, to attract more readership

by Anagha Yoganand BouT Chairman 2019-2020 The 26th year of BouT has been a rather special one. It was a year of uncertainty, a year that changed the normal status quo of work-life culture and finally a year that tested the true resilience of humankind. If you haven’t already guessed it, this was the year of the pandemic ‘COVID-19’. The 26th board was appointed virtually and will be signing-off virtually. The year has no doubt been a tough one circumstantially, but for BouT it has been a rather productive one. We are happy and proud when we look

and engaging BouT followers on its very first virtual tour in collaboration with the Study Trip committee The determination and acquisition skills demonstrated by the company relations committee in collaboration with the Debut committee has resulted in the signing of 14 new company partners. To strengthen the ties between the Alumni and the current building technology students, a new event series of ’Coffee with Alumni’ was initiated this year by the board. In addition to this, like every year, in collaboration with our company partners, many lunch lectures and

back at our achievements in the past year.

workshops were made possible by the events committee.

With the enthusiastic zeal of the education committee,

symposium, COUNTDOWN to a carbon positive future.

BouT published BT Bundle (a compilation of Building Technology graduation thesis posters) and Course repository (a compilation of the projects carried out in Building Technology courses and studios) in collaboration with RuMoer to help BT student’s during enrollments. The board this year also took the bold

Finally, the 26th BouT year closes with a powerful themed Apart from the internal achievements of BouT, this year we collaborated extensively with other master associations through the BouwHouse platform for the Master Introduction event to welcome new students through a pub quiz about Bouwkunde and a virtual

Technology coordinator Peter Teeuw, the enthusiasm of

give insights into the career path of Building Technology

our company partners, and the corporation of all other

to the Bachelor students at Bouwkunde. The success of

master track associations (Argus, Boss, Geos, Polis and

this event is a result of the collaboration between BouT

Stylos).

BouT

treasure hunt around Delft, and Master Symposium to

and AE&T department. With immense support by BouT’s honorary member Marcel Bilow.

As chair, I absolutely enjoyed working with the team (Aditya, Maimuna, Neha, Sophie, Twinkle and Yamini)

Furthermore, this year we released a new edition of

and leading us successfully through a challenging yet

the Building Technology hoodie and have contributed

eventful 26th year. I am certain that, if the seven of us

towards a ‘FAQs’ section for the Building Technology

can flourish in a virtual work environment, we can do

Track for the TU Delft website.

wonders in a physical one and a bright future awaits us all. Having said this, the 26th BouT board proudly

All in all BouT Board 26 has truly been a highly motivated

passes on the baton to the next board that will take on

team of seven members who have thrived in each of

responsibilities from the 9th of April. The new 27th Board

their roles despite the challenges of working remotely

installed is a fun bunch of individuals who will certainly

amidst the pandemic. None of the above-mentioned

add value to the 26 years of BouT’s legacy, keep patient

achievements would have been possible without the

for you will hear from them in our next publication.

dedication of our committee members, the support of all professors in the AE&T department especially Building

Cheers!

87 Figure 1: BouT board 26

76. Generative Design

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