Generative design

Generative design in architecture is an iterative design process that enables architects to explore a wider solution space with more possibility and creativity.{{cite journal | last1= Krish| first1= Sivam| title= A practical generative design method| journal= Computer-Aided Design| volume= 43| number=1|pages= 88–100|year=2011|doi= 10.1016/j.cad.2010.09.009}} Architectural design has long been regarded as a wicked problem.{{cite journal |last1=Rittel |first1=Horst W. J. |first2=Melvin M. |last2=Webber |title=Dilemmas in a General Theory of Planning |journal=Policy Sciences |year=1973 |volume=4 |issue=2 |pages=155–169 |url=http://www.uctc.net/mwebber/Rittel+Webber+Dilemmas+General_Theory_of_Planning.pdf |url-status=dead |archive-url=https://web.archive.org/web/20070930021510/http://www.uctc.net/mwebber/Rittel+Webber+Dilemmas+General_Theory_of_Planning.pdf |archive-date=30 September 2007 |doi=10.1007/bf01405730|s2cid=18634229 }} Compared with traditional top-down design approach, generative design can address design problems efficiently, by using a bottom-up paradigm that uses parametric defined rules to generate complex solutions. The solution itself then evolves to a good, if not optimal, solution.{{cite journal | last1 = Mitchell |first1 = Melanie | last2 = Taylor | first2 = Charles E| title = Evolutionary computation: an overview. | journal = Annual Review of Ecology and Systematics| volume=30|number=1| pages=593–616| year=1999|doi = 10.1146/annurev.ecolsys.30.1.593 |bibcode = 1999AnRES..30..593M }} The advantage of using generative design as a design tool is that it does not construct fixed geometries, but take a set of design rules that can generate an infinite set of possible design solutions. The generated design solutions can be more sensitive, responsive, and adaptive to the problem.

Generative design involves rule definition and result analysis which are integrated with the design process.{{cite journal| title=Towards integrated performance-driven generative design tools| last1= Shea |first1= Kristina | author1-link=Kristina Shea | last2= Aish | first2= Robert| last3= Gourtovaia | first3= Marina|journal=Automation in Construction|volume=14|number=2| pages=253–264|year=2005| doi= 10.1016/j.autcon.2004.07.002 }} By defining parameters and rules, the generative approach is able to provide optimized solution for both structural stability and aesthetics. Possible design algorithms include cellular automata, shape grammar, genetic algorithm, space syntax, and most recently, artificial neural network. Due to the high complexity of the solution generated, rule-based computational tools, such as finite element method and topology optimisation, are more preferable to evaluate and optimise the generated solution.{{cite journal| title = Geometric constraints for shape and topology optimization in architectural design| last1= Dapogny| first1= Charles |last2= Faure|first2= Alexis |last3= Michailidis|first3= Georgios |last4= Allaire|first4= Grégoire|last5= Couvelas|first5= Agnes |last6= Estevez|first6= Rafael|journal = Computational Mechanics|volume=59|number=6|pages=933–965|year=2017|doi = 10.1007/s00466-017-1383-6| bibcode= 2017CompM..59..933D| s2cid= 41570887| url= https://hal.archives-ouvertes.fr/hal-01354004/file/S%26T_architecture_v5.pdf}} The iterative process provided by computer software enables the trial-and-error approach in design, and involves architects interfering with the optimisation process.

Historical precedent work includes Antoni Gaudí's Sagrada Família, which used rule based geometrical forms for structures,{{cite journal | last =Hernandez|first= Carlos Roberto Barrios|title=Thinking parametric design: introducing parametric Gaudi| journal=Design Studies|volume=27|number=3|pages=309–324|year=2006|doi= 10.1016/j.destud.2005.11.006}} and Buckminster Fuller's Montreal Biosphere where the rules to generate individual components is designed, rather than the final product.{{cite book|title=A Fuller explanation: The synergetic geometry of R. Buckminster Fuller| last= Edmondson| first= Amy C| year=2012| publisher=Springer Science & Business Media|chapter= Structure and pattern integrity|pages= 54–60|isbn= 978-0-8176-3338-7 |url=http://www.arvindguptatoys.com/arvindgupta/synergy-fuller.pdf|doi= 10.1007/978-1-4684-7485-5}}

More recent generative design cases include Foster and Partners' Queen Elizabeth II Great Court, where the tessellated glass roof was designed using a geometric schema to define hierarchical relationships, and then the generated solution was optimized based on geometrical and structural requirement.{{cite conference| title=The analytic and numerical definition of the geometry of the British Museum Great Court Roof|last= Williams| first= Chris JK|volume=200|pages=434–440|year=2001| conference= Proceedings of mathematics & design 2001: the third international conference|editor1-last= Burry|editor1-first = Mark|editor2-last= Datta|editor2-first= Sambit|editor3-last= Dawson| editor3-first= Anthony |editor4-last= Rollo|editor4-first= John|publisher=Deakin University|location= Geelong Vic Australia|isbn=0-7300-2526-8|url= http://www.math.chalmers.se/Math/Grundutb/CTH/mve275/1314/Chris2.pdf}}

Generative design in sustainable design is an effective approach addressing energy efficiency and climate change at the early design stage, recognizing buildings contribute to approximately one-third of global greenhouse gas emissions and 30%-40% of total building energy use.{{Cite journal |last1=Suphavarophas |first1=Phattranis |last2=Wongmahasiri |first2=Rungroj |last3=Keonil |first3=Nuchnapang |last4=Bunyarittikit |first4=Suphat |date=May 2024 |title=A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings |journal=Buildings |language=en |volume=14 |issue=5 |pages=1311 |doi=10.3390/buildings14051311 |doi-access=free |issn=2075-5309}} It integrates environmental principles with algorithms, enabling exploration of countless design alternatives to enhance energy performance, reduce carbon footprints, and minimize waste.

A key feature of generative design in sustainable design is its ability to incorporate Building Performance Simulations (BPS) into the design process. Simulation programs like [https://energyplus.net/ EnergyPlus], [https://www.ladybug.tools/ Ladybug Tools], and so on, combined with generative algorithms, can optimize design solutions for cost-effective energy use and zero-carbon building designs. For example, the GENE_ARCH system used a Pareto algorithm with [https://www.doe2.com/ DOE2.1E building energy simulation] for the whole building design optimization.{{Cite journal |last=Caldas |first=Luisa |date=2008-01-01 |title=Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system |url=https://linkinghub.elsevier.com/retrieve/pii/S1474034607000493 |journal=Advanced Engineering Informatics |series=Intelligent computing in engineering and architecture |volume=22 |issue=1 |pages=59–70 |doi=10.1016/j.aei.2007.08.012 |issn=1474-0346}} Generative design has improved sustainable facade design, as illustrated by the algorithm of cellular automata and daylight simulations in adaptive facade design.{{Cite journal |last=Kim |first=Jieun |date=2013-04-21 |title=Adaptive façade design for the daylighting performance in an office building: the investigation of an opening design strategy with cellular automata |url=https://academic.oup.com/ijlct/article/10/3/313/702664 |journal=International Journal of Low-Carbon Technologies |volume=10 |issue=3 |pages=313–320 |doi=10.1093/ijlct/ctt015 |issn=1748-1317}} In addition, genetic algorithms were used with radiation simulations for energy-efficient PV modules on high-rise building facades.{{Cite journal |last1=Vahdatikhaki |first1=Faridaddin |last2=Salimzadeh |first2=Negar |last3=Hammad |first3=Amin |date=2022-03-01 |title=Optimization of PV modules layout on high-rise building skins using a BIM-based generative design approach |url=https://linkinghub.elsevier.com/retrieve/pii/S0378778821010719 |journal=Energy and Buildings |volume=258 |pages=111787 |doi=10.1016/j.enbuild.2021.111787 |bibcode=2022EneBu.25811787V |issn=0378-7788}} Generative design is also applied to life cycle analysis (LCA), as demonstrated by a framework using grid search algorithms to optimize exterior wall design for minimum environmental embodied impact.{{Cite journal |last1=Hassan |first1=Sally R. |last2=Megahed |first2=Naglaa A. |last3=Abo Eleinen |first3=Osama M. |last4=Hassan |first4=Asmaa M. |date=2022-07-15 |title=Toward a national life cycle assessment tool: Generative design for early decision support |url=https://linkinghub.elsevier.com/retrieve/pii/S0378778822003152 |journal=Energy and Buildings |volume=267 |pages=112144 |doi=10.1016/j.enbuild.2022.112144 |bibcode=2022EneBu.26712144H |issn=0378-7788}}

Multi-objective optimization embraces multiple diverse sustainability goals, such as interactive kinetic louvers using biomimicry and daylight simulations to enhance daylight, visual comfort and energy efficiency.{{Cite journal |last1=Hosseini |first1=Seyed Morteza |last2=Heiranipour |first2=Milad |last3=Wang |first3=Julian |last4=Hinkle |first4=Laura Elizabeth |last5=Triantafyllidis |first5=Georgios |last6=Attia |first6=Shady |date=2024-05-19 |title=Enhancing Visual Comfort and Energy Efficiency in Office Lighting Using Parametric-Generative Design Approach for Interactive Kinetic Louvers |url=https://solarlits.com/jd/11-69 |journal=Journal of Daylighting |language=en |volume=11 |issue=1 |pages=69–96 |doi=10.15627/jd.2024.5 |issn=2383-8701|doi-access=free }} The study of PV and shading systems can maximize on-site electricity, improve visual quality and daylight performance.{{Cite journal |last1=Banti |first1=Neri |last2=Ciacci |first2=Cecilia |last3=Bazzocchi |first3=Frida |last4=Di Naso |first4=Vincenzo |date=September 2024 |title=Enhancing Industrial Buildings' Performance through Informed Decision Making: A Generative Design for Building-Integrated Photovoltaic and Shading System Optimization |journal=Solar |language=en |volume=4 |issue=3 |pages=401–421 |doi=10.3390/solar4030018 |doi-access=free |issn=2673-9941|hdl=2158/1375896 |hdl-access=free }}

AI and machine learning (ML) further improve computation efficiency in complex climate-responsive sustainable design. one study employed reinforcement learning to identify the relationship between design parameters and energy use for a sustainable campus,{{Cite journal |last1=Chang |first1=Soowon |last2=Saha |first2=Nirvik |last3=Castro-Lacouture |first3=Daniel |last4=Yang |first4=Perry Pei-Ju |date=2019-09-01 |title=Multivariate relationships between campus design parameters and energy performance using reinforcement learning and parametric modeling |url=https://linkinghub.elsevier.com/retrieve/pii/S0306261919307767 |journal=Applied Energy |volume=249 |pages=253–264 |doi=10.1016/j.apenergy.2019.04.109 |bibcode=2019ApEn..249..253C |issn=0306-2619}} while some other studies tried hybrid algorithms, such as using the genetic algorithm and GANs to balance daylight illumination and thermal comfort under different roof conditions.{{Cite journal |last1=Yu |first1=Zhongqi |last2=Ge |first2=Xinyi |last3=Fan |first3=Zhaoxiang |last4=Zhou |first4=Yihang |last5=Lin |first5=Dawei |date=2024-10-15 |title=Optimization framework for daylight and thermal environment of retractable roof natatoriums based on generative adversarial network and genetic algorithm |url=https://linkinghub.elsevier.com/retrieve/pii/S0378778824008119 |journal=Energy and Buildings |volume=321 |pages=114695 |doi=10.1016/j.enbuild.2024.114695 |issn=0378-7788}} Other popular AI tools were also integrated, including deep reinforcement learning (DRL) and computer vision (CV) to generate an urban block according to direct sunlight hours and solar heat gains.{{Cite book |last1=Han |first1=Zhen |last2=Yan |first2=Wei |last3=Liu |first3=Gang |chapter=A Performance-Based Urban Block Generative Design Using Deep Reinforcement Learning and Computer Vision |date=2021 |editor-last=Yuan |editor-first=Philip F. |editor2-last=Yao |editor2-first=Jiawei |editor3-last=Yan |editor3-first=Chao |editor4-last=Wang |editor4-first=Xiang |editor5-last=Leach |editor5-first=Neil |title=Proceedings of the 2020 DigitalFUTURES |chapter-url=https://link.springer.com/chapter/10.1007/978-981-33-4400-6_13 |language=en |location=Singapore |publisher=Springer |pages=134–143 |doi=10.1007/978-981-33-4400-6_13 |isbn=978-981-334-400-6}} These AI-driven generative design methods enable faster simulations and design decision making, resulting in designs that are environmentally responsible.

Additive manufacturing (AM) is a process that creates physical models directly from 3D data by joining materials layer by layer. It is used in industries to produce a variety of end-use parts, which are final components designed for direct application in products or systems. AM provides design flexibility and enables material reduction in lightweight applications, such as aerospace, automotive, medical, and portable electronic devices, where minimizing weight is critical for performance. Generative design, one of the four key methods for lightweight design in AM, is commonly applied to optimize structures for specific performance requirements.{{Cite journal |last=Vaneker |first=Tom |last2=Bernard |first2=Alain |last3=Moroni |first3=Giovanni |last4=Gibson |first4=Ian |last5=Zhang |first5=Yicha |date=2020-01-01 |title=Design for additive manufacturing: Framework and methodology |url=https://linkinghub.elsevier.com/retrieve/pii/S0007850620301396 |journal=CIRP Annals - Manufacturing Technology |volume=69 |issue=2 |pages=578–599 |doi=10.1016/j.cirp.2020.05.006 |issn=0007-8506|hdl=11311/1145339 |hdl-access=free }}

Generative design can help create optimized solutions that balance multiple objectives, such as enhancing performance while minimizing cost.{{Cite journal |last=Plocher |first=János |last2=Panesar |first2=Ajit |date=2019-12-05 |title=Review on design and structural optimisation in additive manufacturing: Towards next-generation lightweight structures |url=https://linkinghub.elsevier.com/retrieve/pii/S0264127519306021 |journal=Materials & Design |volume=183 |pages=108164 |doi=10.1016/j.matdes.2019.108164 |issn=0264-1275|doi-access=free |hdl=10044/1/73026 |hdl-access=free }} In design for additive manufacturing (DfAM), multi-objective topology optimization is used to generate a set of candidate solutions. Designers then assess these options using their expertise and key performance indicators (KPIs) to select the best option for implementation.

However, integrating AM constraints (e.g.,speed of build, materials, build envelope, and accuracy) into generative design remains challenging, as ensuring all solutions are valid is complex. Balancing multiple design objectives while limiting computational costs adds further challenges for designers.{{Cite journal |last=Wang |first=Zhiping |last2=Zhang |first2=Yicha |last3=Bernard |first3=Alain |date=2021-05-01 |title=A constructive solid geometry-based generative design method for additive manufacturing |url=https://linkinghub.elsevier.com/retrieve/pii/S2214860421001172 |journal=Additive Manufacturing |volume=41 |pages=101952 |doi=10.1016/j.addma.2021.101952 |issn=2214-8604|doi-access=free }} To overcome these difficulties, researchers proposed a generative design method with manufacturing validation to improve decision-making efficiency. This method starts with a constructive solid geometry (CSG)-based technique to create smooth topology shapes with precise geometric control. Then, a genetic algorithm is used to optimize these shapes, and the method offers designers a set of top non-dominated solutions on the Pareto front for further evaluation and final decision-making. By combining multiple techniques, this method can generate many high-quality solutions with smooth boundaries at lower computational costs, making it a practical approach for designing lightweight structures in AM.

Building on topology optimization methods, software providers introduced generative design features in their tools, helping designers set criteria and rank solutions. Industry is driving advancements in generative design for AM, highlighting the need for tools that not only offer a range of solution choices but also streamline workflows for industrial use.

• Computer art
• Computer-automated design
• Feedback
• Generative art
• Parametric design
• Procedural modeling
• Random number generation
• System dynamics
• Topology optimization

{{Reflist}}

• Gary William Flake: The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems, and Adaptation. MIT Press 1998, {{ISBN|978-0-262-56127-3}}
• John Maeda: Design by Numbers, MIT Press 2001, {{ISBN|978-0-262-63244-7}}
• {{cite journal |doi=10.1016/j.cad.2010.09.009 |title=A practical generative design method |journal=Computer-Aided Design |volume=43 |pages=88–100 |year=2011 |last1=Krish |first1=Sivam }}
• Celestino Soddu: papers on Generative Design (1991–2011) at [http://www.generativedesign.com/papers.html Generative Art Design Papers. C.Soddu, E.Colabella]

{{Design}}

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Category:Computer graphics