Balancing design intent and performance: an algorithmic design approach

References

• Aguiar, Rita, Carmo Cardoso, and António Leitão. 2017. “Algorithmic Design and Analysis Fusing Disciplines.” In Proceedings of the 37th ACADIA Conference, 28–37. https://doi.org/10.52842/conf.acadia.2017.028.
   Google Scholar
• Anton, Ionuţ, and Daniela Tănase. 2016. “Informed Geometries. Parametric Modelling and Energy Analysis in Early Stages of Design.” Energy Procedia 85:9–16. https://doi.org/10.1016/j.egypro.2015.12.269.
   Google Scholar
• Baker, William, Alessandro Beghini, Juan Carrion, and Cenk Tort. 2009. “Scripting for a New Architecture: Studies in Structural Optimization.” In Proceedings of the 29th ACADIA Conference, 252–254. https://doi.org/10.52842/conf.acadia.2009.252.
   Google Scholar
• Bao, Ding Wen, Xin Yan, Roland Snooks, and Yimin Xie. 2021. “SwarmBESO: Multi-Agent and Evolutionary Computational Design Based on the Principles of Structural Performance.” In Proceedings of the 26th CAADRIA Conference, 241–250.
   Google Scholar
• Barraza, Manuel, Edén Bojórquez, Eduardo Fernández-González, and Alfredo Reyes-Salazar. 2017. “Multi-Objective Optimization of Structural Steel Buildings Under Earthquake Loads Using NSGA-II and PSO.” KSCE Journal of Civil Engineering 21 (2): 488–500. https://doi.org/10.1007/s12205-017-1488-7.
   Web of Science ®Google Scholar
• Belém, Catarina Garcia. 2019. Optimization of Time-Consuming Objective Functions: Derivative-Free Approaches and their Application in Architecture. Instituto Superior Técnico, University of Lisbon.
   Google Scholar
• Belém, Catarina, and António Leitão. 2018. “From Design to Optimized Design An Algorithmic-Based Approach.” In Proceedings of the 36th eCAADe Conference, 559–568.
   Google Scholar
• Bertagna, Federico, Pierluigi D’Acunto, Patrick Ole Ohlbrock, and Vahid Moosavi. 2021. “Holistic Design Explorations of Building Envelopes Supported by Machine Learning.” Journal of Facade Design and Engineering 9 (1): 31–46.
   Google Scholar
• Bialkowski, Sebastian. 2016. “Structural Optimisation Methods as a New Toolset for Architects.” In Proceedings of the 34th eCAADe Conference, 255–264. https://doi.org/10.52842/conf.ecaade.2016.2.255.
   Google Scholar
• Bialkowski, Sebastian. 2017. “TOpos - GPGPU Accelerated Structural Optimisation Utility for Architects.” In Proceedings of the 35th eCAADe Conference, 679–688. https://doi.org/10.52842/conf.ecaade.2017.1.679.
   Google Scholar
• Boswell, C. Keith. 2013. Exterior Building Enclosures: Design Process and Composition for Innovative Facades. John Wiley & Sons, Inc.
   Google Scholar
• Bouchlaghem, N. 2000. “Optimizing the Design of Building Envelopes for Thermal Performance.” Automation in Construction 10 (1): 101–112. https://doi.org/10.1016/S0926-5805(99)00043-6.
   Web of Science ®Google Scholar
• Burry, M. 2003. “Between Intuition and Process: Parametric Design and Rapid Prototyping.” In Architecture in the Digital Age: Design and Manufacturing, edited by Branko Kolarevic, 149–162. New York: Taylor&Francis.
   Google Scholar
• Caetano, Inês, and António Leitão. 2021. “Mathematically Developing Building Facades: An Algorithmic Framework.” In Formal Methods in Architecture: Advances in Science, Technology & Innovation (IEREK Interdisciplinary Series for Sustainable Development), 3–17. Cham: Springer.
   Google Scholar
• Caetano, Inês, António Leitão, and Francisco Bastos. 2020. “From Architectural Requirements to Physical Creations.” Journal of Façade Design and Engineering 8 (2): 59–80.
   Google Scholar
• Carlucci, Salvatore, Giulio Cattarin, Francesco Causone, and Lorenzo Pagliano. 2015. “Multi-Objective Optimization of a Nearly Zero-Energy Building Based on Thermal and Visual Discomfort Minimization Using a Non-Dominated Sorting Genetic Algorithm (NSGA-II).” Energy and Buildings 104:378–394. https://doi.org/10.1016/j.enbuild.2015.06.064.
   Web of Science ®Google Scholar
• Ciardiello, Adriana, Federica Rosso, Jacopo Dell’Olmo, Virgilio Ciancio, Marco Ferrero, and Ferdinando Salata. 2020. “Multi-Objective Approach to the Optimization of Shape and Envelope in Building Energy Design.” Applied Energy 280 (October). Elsevier Ltd.
   Google Scholar
• Davis, Daniel, Jane Burry, and Mark Burry. 2011. “The Flexiblity of Logic Programming: Parametrically Regenerating the Sagrada Família.” In Proceedings of the 16th CAADRIA Conference, 29–38. https://doi.org/10.52842/conf.caadria.2011.029.
   Google Scholar
• Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.” IEEE Transactions on Evolutionary Computation 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
   Web of Science ®Google Scholar
• Elghandour, Aya, Ahmed Saleh, Osama Aboeineen, and Ashraf Elmokadem. 2016. “Using Parametric Design to Optimize Building’s Façade Skin to Improve Indoor Daylighting Performance.” In Proceedings of the 3rd IBPSA-England Conference BSO, 353–361.
   Google Scholar
• Evins, Ralph. 2013. “A Review of Computational Optimisation Methods Applied to Sustainable Building Design.” In Renewable and Sustainable Energy Reviews 22, 230–245. Elsevier.
   Google Scholar
• Evins, Ralph, Sam Conrad Joyce, Philip Pointer, Shrikant Sharma, Ravi Vaidyanathan, and Chris Williams. 2012. “Multi-Objective Design Optimisation: Getting More for Less.” Proceedings of the Institution of Civil Engineers 165 (5): 5–10. https://doi.org/10.1680/cien.11.00014.
   Google Scholar
• Fagerstrom, Gustav, Erik Verboon, and Robert Aish. 2014. “Topo-Façade: Envelope Design and Fabrication Planning Using Topological Mesh Representations.” In Fabricate 2014: Negotiating Design & Making, 173–179. UCL Press.
   Google Scholar
• Fernando, Ruwan, Robin Drogemuller, F. D. Salim, and Jane Burry. 2010. “Patterns, Heuristics for Architectural Design Support: Making Use of Evolutionary Modelling in Design.” In Proceedings of the 15th CAADRIA Conference, 283–292. https://doi.org/10.52842/conf.caadria.2010.283.
   Google Scholar
• Gagne, Jaime M.L., and Marilyne Andersen. 2010. “Multi-Objective Optimization for Daylighting Design Using a Genetic Algorithm.” In Proceedings of SimBuild 2010 - 4th National Conference of IBPSA-USA.
   Google Scholar
• Gagne, Jaime, and Marilyne Andersen. 2012. “A Generative Facade Design Method Based on Daylighting Performance Goals.” Journal of Building Performance Simulation 5 (3): 141–154. https://doi.org/10.1080/19401493.2010.549572.
   Web of Science ®Google Scholar
• Harding, John E., and Paul Shepherd. 2017. “Meta-Parametric Design.” Design Studies 52:73–95. https://doi.org/10.1016/j.destud.2016.09.005.
   Web of Science ®Google Scholar
• Herr, Christiane M., Davide Lombardi, and Isaac Galobardes. 2018. “Parametric Design of Sculptural Fibre Reinforced Concrete Facade Components.” In Proceedings of the 23rd CAADRIA Conference, 319–328. https://doi.org/10.52842/conf.caadria.2018.2.319.
   Google Scholar
• Hofmeyer, Herm, Thijs D. E. Goede, and Sjonnie Boonstra. 2021. “Co-Evolutionary Spatial-Structural Building Design Optimisation Including Facade Openings.” In Proceedings of the 26th CAADRIA Conference, 431–440. https://doi.org/10.52842/conf.caadria.2021.1.431.
   Google Scholar
• Huang, Yu, and Jian-lei Niu. 2015. “Optimal Building Envelope Design Based on Simulated Performance: History, Current Status and New Potentials.” In Energy and Buildings 117, 387–398. Elsevier B.V.
   Google Scholar
• Janssen, Patrick. 2005. A Design Method and Computational Architecture for Generating and Evolving Building Designs. Hong Kong Polytechnic University.
   Google Scholar
• Janssen, Patrick. 2014. “Visual Dataflow Modelling: Some thoughts on complexity.” Fusion: Proceedings of the 32nd eCAADe Conference 2:547–556.
   Google Scholar
• Jin, Qian, and Mauro Overend. 2014. “A Prototype Whole-Life Value Optimization Tool for Façade Design.” Journal of Building Performance Simulation 7 (3): 217–232.
   Web of Science ®Google Scholar
• Johan, Ryan, Michael Chernyavsky, Alessandra Fabbri, Nicole Gardner, M. Hank Haeusler, and Yannis Zavoleas. 2019. “Building Intelligence through Generative Design.” In Intelligent and Informed: Proceedings of the 24th CAADRIA Conference, 371–380.
   Google Scholar
• Kasinalis, C., R. C. G. M. Loonen, D. Cóstola, and J. L. M. Hensen. 2014. “Framework for Assessing the Performance Potential of Seasonally Adaptable Facades Using Multi-Objective Optimization.” Energy and Buildings 79:106–113. https://doi.org/10.1016/j.enbuild.2014.04.045.
   Web of Science ®Google Scholar
• Khazaii, J. 2016. “11: Pareto-based optimization.” In Advanced Decision Making for HVAC Engineers: Creating Energy Efficient Smart Buildings, 99–115. Springer.
   Google Scholar
• Kolarevic, Branko. 2003. “Computing the Performative in Architecture.” In Proceedings of the 21st eCAADe Conference, 457–464. https://doi.org/10.52842/conf.ecaade.2003.457.
   Google Scholar
• Krolikowski, Dirk, and Damian Eley. 2014. “The Leadenhall Building: Design for Fabrication-Digital Workflow and Downstream Fabrication System Performance.” In Fabricate 2014: Negotiating Design & Making, 68–75. UCL Press.
   Google Scholar
• Lee, Juney, Corentin Fivet, and Caitlin Mueller. 2015. “Modelling with Forces: Grammar-Based Graphic Statics for Diverse Architectural Structures.” In Modelling Behaviour: Design Modelling Symposium, 491–504. Cham: Springer.
   Google Scholar
• Leitão, António, Luís Santos, and José Lopes. 2012. “Programming Languages for Generative Design: A Comparative Study.” International Journal of Architectural Computing 10 (1): 139–162. https://doi.org/10.1260/1478-0771.10.1.139.
   Google Scholar
• Machairas, Vasileios, Aris Tsangrassoulis, and Kleo Axarli. 2014. “Algorithms for Optimization of Building Design: A Review.” Renewable and Sustainable Energy Reviews 31 (1364): 101–112. https://doi.org/10.1016/j.rser.2013.11.036.
   Web of Science ®Google Scholar
• Muehlbauer, Manuel. 2018. Typogenetic Design - Aesthetic Decision Support for Architectural Shape Generation. RMIT University.
   Google Scholar
• Muehlbauer, Manuel, Andy Song, and Jane Burry. 2020. “Smart Structures - A Generative Design Framework for Aesthetic Guidance in Structural Node Design Application of Typogenetic Design for Custom-Optimisation of Structural.” In Proceedings of the 38th eCAADe Conference, 23–632.
   Google Scholar
• Mueller, Caitlin T. 2014. Computational Exploration of the Structural Design Space. Massachusetts Institute of Technology June.
   Google Scholar
• Nguyen, Anh-Tuan, Sigrid Reiter, and Philippe Rigo. 2014. “A Review on Simulation-Based Optimization Methods Applied to Building Performance Analysis.” Applied Energy 113 (Elsevier Ltd): 1043–1058. https://doi.org/10.1016/j.apenergy.2013.08.061.
   Google Scholar
• Ochoa, Carlos Ernesto, and Isaac Guedi Capeluto. 2009. “Advice Tool for Early Design Stages of Intelligent Facades Based on Energy and Visual Comfort Approach.” Energy and Buildings 41 (5): 480–488. https://doi.org/10.1016/j.enbuild.2008.11.015.
   Web of Science ®Google Scholar
• Pantazis, Evangelos, and David Gerber. 2018. “A Framework for Generating and Evaluating Façade Designs Using a Multi-Agent System Approach.” International Journal of Architectural Computing 16 (4): 248–270. https://doi.org/10.1177/1478077118805874.
   Web of Science ®Google Scholar
• Pereira, Inês, and António Leitão. 2020a. “More Is More: The No Free Lunch Theorem in Architecture.” In Proceedings of the ASA Conference, 765–774.
   Google Scholar
• Pereira, Inês, and António Leitão. 2020b. “The Cost of Daylight: A Parallelized Approach to Multi-Objective Optimization.” In Proceedings of the 35th PLEA Conference.
   Google Scholar
• Piermarini, Emidio, Hayden Nuttall, Rob May, Victoria M. Janssens, Wolf Manglesdorf, and Tim Kelly. 2018. “Morpheus Hotel, Macau – a Paradigm Shift in Computational Engineering.” Steel Construction 11 (3): 218–231. https://doi.org/10.1002/stco.201810025.
   Google Scholar
• Romero, Fernando, and Armando Ramos. 2013. “Bridging a Culture: The Design of Museo Soumaya.” In Architectural Design Magazine: Computation Works: The Buidings of Algorithmic Thought, Vol. 83, 66–69. Wiley Online Library.
   Google Scholar
• Sammer, Maria, António Leitão, and Inês Caetano. 2019. “From Visual Input to Visual Output in Textual Programming.” In Proceedings of the 24th CAADRIA Conference, 645–654.
   Google Scholar
• Schultz, Joshua, and Neil Katz. 2018. “Origami-Inspired Facade Design: Parametric Studies for Architectural and Structural Efficiency.” In Facade Tectonics 2018 World Congress, 349–358.
   Google Scholar
• Senses, N. 2007. Foam Structures: A Comparative Structural Efficiency Analysis Based on the Building Case ‘Watercube’. TU Vienna.
   Google Scholar
• Shi, Xing. 2010. “Performance-Based and Performance-Driven Architectural Design and Optimization.” Frontiers of Architecture and Civil Engineering in China 4 (4): 512–518. https://doi.org/10.1007/s11709-010-0090-6.
   Google Scholar
• Stevanović, Sanja. 2013. “Optimization of Passive Solar Design Strategies: A Review.” Renewable and Sustainable Energy Reviews 25:177–196. https://doi.org/10.1016/j.rser.2013.04.028.
   Web of Science ®Google Scholar
• Terzidis, Kostas. 2006. Algorithmic Architecture. Edited by Kostas Terzidis. 1st ed. Oxford, UK: Elsevier Ltd.
   Google Scholar
• Touloupaki, Eleftheria, and Theodoros Theodosiou. 2017. “Performance Simulation Integrated in Parametric 3D Modeling as a Method for Early Stage Design Optimization - A Review.” Energies 10 (637).
   Google Scholar
• Turrin, Michela, Peter Von Buelow, and Rudi Stouffs. 2011. “Design Explorations of Performance Driven Geometry in Architectural Design Using Parametric Modeling and Genetic Algorithms.” Advanced Engineering Informatics 25 (4) Elsevier Ltd: 656–675. https://doi.org/10.1016/j.aei.2011.07.009
   Web of Science ®Google Scholar
• Wang, Weimin, Radu Zmeureanu, and Hugues Rivard. 2005. “Applying Multi-Objective Genetic Algorithms in Green Building Design Optimization.” Building and Environment 40 (11): 1512–1525. https://doi.org/10.1016/j.buildenv.2004.11.017.
   Web of Science ®Google Scholar
• Warton, James, Heath May, and Rodovan Kovacevic. 2017. “Automated Design-to-Fabrication for Architectural Envelopes: A stadium skin case study.” In Fabricate 2017, 36–43. UCL Press.
   Google Scholar
• Wortmann, Thomas. 2019. “Genetic Evolution vs. Function Approximation: Benchmarking Algorithms for Architectural Design Optimization.” Journal of Computational Design and Engineering 6 (3): 414–428. https://doi.org/10.1016/j.jcde.2018.09.001.
   Web of Science ®Google Scholar
• Wortmann, Thomas, and Thomas Fischer. 2020. “Does Architectural Design Optimization Require Multiple Objectives? A Critical Analysis.” In Proceedings of the 25th CAADRIA Conference, 365–374. https://doi.org/10.52842/conf.caadria.2020.1.365.
   Google Scholar
• Wortmann, Thomas, and Bige Tunçer. 2017. “Differentiating Parametric Design: Digital workflows in contemporary architecture and construction.” Design Studies 52:173–197. https://doi.org/10.1016/j.destud.2017.05.004.
   Web of Science ®Google Scholar
• Wortmann, Thomas, Christoph Waibel, Giacomo Nannicini, Ralph Evins, Thomas Schroepfer, and Jan Carmeliet. 2017. “Are Genetic Algorithms Really the Best Choice in Building Energy Optimization?” In SIMAUD ‘17 Proceedings, 51–58.
   Google Scholar
• Xie, Xiaohuan, and Zhonghua Gou. 2017. “Building Performance Simulation as an Early Intervention or Late Verification in Architectural Design: Same Performance Outcome but Different Design Solutions.” Journal of Green Building 12 (1): 45–61. https://doi.org/10.3992/1552-6100.12.1.45.
   Web of Science ®Google Scholar