Journal of South Architecture

Application of Machine Learning in Architectural Design-a Review

MAChenlong (Architectural Design and Research Institute Co., Ltd., South China University of Technology; State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology), ZHUShuyan (School of Architecture, South China University ofechnology; State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology), WANGMingjie (Architectural Design and Research Institute Co., Ltd., South China University of Technology; State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology)

Abstract


This paper first clarifies the two popular concepts of “machine learning”and “neural network,”combs the current cutting-edge research in the field of architectural design, then introduces the interface tools needed from the perspective of architectural design practice, and looks forward to the trend of application in the future.

Keywords


machine learning; neural network; architectural design; cutting-edge research

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References


JIAO LiCheng, YANG ShuYuan, LIU Fang, et al. Seventy Years Beyond Neural Networks: Retrospect and Prospect [J] . Chinese Journal of Computers, 2016, 39( 8): 1697-1716.

ZHOU ZhiHua. MACHINE Learning[M] . 1st Edition. Beijing: Tsinghua University Press, 2016.

HASTIE T, TIBSHIRANI R, FRIEDMAN J. Elements of Statistical Learning 2nd ed.[M] . Elements, 2009.

KOHONEN T. An Introduction to Neural Computing[J] . Neural Networks, 1988.

CHEN C. CiteSpace II: Detecting and Visualizing Emerging Trends and Transient patterns in scientific literature[J] . Journal of the American Society for Information Science and Technology,2006.

BESS KRIETEMEYER, JASON DEDRICK, CAMILA ANDINO D A. Spatial Interpolation of Outdoor Illumination at Night using Geostatistical Modeling[C] ∥Proceedings of the SimAUD 2019. 2020: 61-68.

BROWN N C, MUELLER C T. Design Variable Analysis and Generation for Performance-Based Parametric Modeling in Architecture [ J ] . International Journal of Architectural Computing, 2019, 17( 1): 36-52.

SJOBERG C H. Emergent Syntax-Machine Learning Models for Directed Curation of Design Solution Space[J] . ProQuest Dissertations and Theses, 2017: 54.

YUAN Feng, LIN YuQiong. Research on High-Rise Building Group Morphology Generative Design Method Based on Physical Wind Tunnel and Neural Network Algorithm [J] . Journal of Human Settlements in West China, 2019, 34( 1): 22-30.

YETKIN O, MOON K, PYTHON G, et al. A Novel Approach for Classification of Structural Elements in a 3D Model by Supervised Learning[J] . eCAADe201, 2018.

RHEE J, LLACH D C, KRISHNAMURTI R. Context-rich Urban Analysis Using Machine Learning A case study in Pittsburgh, [C] ∥Blucher Design Proceedings. São Paulo: Editora Blucher, 2019: 343-352.

YOUSIF S, YAN W. Shape Clustering Using K-Medoids in Architectural Form Finding [C] ∥ Communications in Computer and Information Science. 2019.

SHERMEEN Y W Y. Application of an Automatic Shape Clustering Method Into Generative and Design Optimization Systems [C] ∥Proceedings of Acadia. 2019: 60-69.

SÁNCHEZ-VAQUERIZO J, CARDOSO L D. The Social Life of Small Urban Spaces 2. 0: Three Experiments in Computational Urban Studies[C] ∥Communications in Computer and Information Science. 2019.

HARDING J. Dimensionality Reduction for Parametric Design Exploration[C] ∥ Advances in Architectural Geometry 2016. 2016.

ZAGHLOUL M. Machine-Learning Aided Architectural Design Introduction: Neural Networks vs . conventional computing[C] ∥Proceedings of Generative art. 2015: 283-293.

OJHA V K, GRIEGO D, KULIGA S, et al. Machine learning Approaches to Understand the Influence of Urban Environments on Human ’s Physiological Response [ J ] . Information Sciences, Elsevier Inc., 2019, 474(October): 154-169.

ZAGHLOUL M. Machine-Learning aided Architectural Design Synthesize Fast CFD by Machine-Learning [D] . ETH Zurich,2017.

BRUGNARO G, HANNA S. Adaptive Robotic Carving Training Methods for the Integration of Material Performances in Timber Manufacturing[J] . Robotic Fabrication in Architecture, Art and Design 2018, 2019.

ROSSI G, NICHOLAS P. Haptic Learning Towards Neural-Network-based adaptive Cobot Path-Planning for unstructured spaces[C] ∥2020.

HEIMIG T, BRELL-COKCAN S. Robotic Constraints Informed Design Process[J] . Acadia 2019, 2019.

WANG Z, SHI J, YU C, et al. Automatic Design of Main Pedestrian Entrance of Building site Based on Machine Learning: A Case Study of Museums in China ’s Urban Environment [J] . CAADRIA 2018-23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, 2018, 2: 227-235.

DANHAIVE R, MUELLER C. Structural Metamodelling of shells[J] . Proceedings of the IASS Symposium 2018, 2018.

ZHENG H. Form Finding and Evaluating Through Machine Learning: The Prediction of Personal Design Preference in Polyhedral Structures [ G] ∥ Proceedings of the 2019 DigitalFUTURES. 2019.

MIGUEL J, VILLAFAÑE M E, PIŠKOREC L, et al. Deep Form Finding Using Variational Autoencoders for Deep Form Finding of Structural Typologies[C] ∥Blucher Design Proceedings. São Paulo: Editora Blucher, 2019: 71-80.

YETKIN O, SORGUÇ A G. Design Space Exploration of Initial Structural Design Alternatives via Artificial Neural Networks[C] ∥2020.

HAO ZHENG, VAHID MOOSAVI M A. Machine Learning Assisted Evaluations in 3D Graphic Statics[C] ∥Proceedings of International Association for Shell and Spatial Structures Annual Symposia (IASS) . 2019.

GIANNOPOULOU E, BAQUERO P, WARANG A, et al. Computational Workflow for Segmented Shell Structures: an ANN Approach for Fabrication Efficiency[C] ∥ IASS Symposium Form and Force. 2019.

ARMAN N, DIEGO P A M. Data Modeling of Cities, a Machine Learning Application[C] ∥Proceedings of SimAUD. 2020: 35-42.

PATERSON G, HONG S M, MUMOVIC D, et al. Real-time Environmental Feedback at the Early Design Stages[J] . Computation and Performance-Proceedings of the 31st eCAADe Conference-Volume 2, Faculty of Architecture, Delft University of Technology, 18-20 September 2013, 2013, 2: 79-86.

SI B, WANG J, YAO X, et al. Multi-Objective Optimization Design of a Complex Building Based on an Artificial Neural Network and Performance Evaluation of Algorithms[J] . Advanced Engineering Informatics, 2019.

JIAXIN Z, YUNQIN L, HAIQING L, et al. Sensitivity Analysis of Thermal Performance of Granary Building based on Machine Learning[C] ∥Proceedings of the CAADRIA. 2019: 665-674.

LIN Y, YAO J, HUANG C, et al. The Future of Environmental Performance Architectural Design Based on Human-Computer Interaction -Prediction Generation Based on Physical Wind Tunnel and Neural Network Algorithms[C] ∥ Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia (Caadria 2019) . 2019: 633-642.

LORENZ C-L, SPAETH A B, DE SOUZA C B, et al. Machine learning in design exploration: An investigation of the sensitivities of ANN-based daylight predictions[C] ∥Proceedings of the 18th International Conference on CAAD Futures. 2019.

WORTMANN T, NATANIAN J. Multi-Objective Optimization for Zero-Energy Urban Design in China: A Benchmark[C] ∥ Proceedings of SimAUD. 2020: 203-210.

YOSHIMURA Y, CAI B, WANG Z, et al. Deep Learning Architect: Classification for Architectural Design Through the Eye of Artificial Intelligence[J] . Lecture Notes in Geoinformation and Cartography, 2019: 249-265.

KIM J S, SONG J Y, LEE J K. Approach to the Extraction of Design Features of Interior Design Elements Using Image Recognition Technique[C] ∥ CAADRIA 2018 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting. 2018.

KATO Y, MATSUKAWA S. Development of Generating System for Architectural Color icons Using Google map Platform and Tensorflow-Segmentation[C] ∥Intelligent and Informed Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia (Caadria 2019) . 2019.

KIM J, SONG J, LEE J-K. Approach to Auto-Recognition of Design Elements for the Intelligent Management of Interior Pictures [C] ∥Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia ( Caadria 2019) . 2019: 785-794.

KIM J, SONG J, LEE J K. Recognizing and Classifying Unknown Object in BIM Using 2D CNN[C] ∥Communications in Computer and Information Science. 2019.

NG J M Y, KHEAN N, MADDEN D, et al. Optimising Image Classification Implementation of Convolutional Neural Network Algorithms to Distinguish Between Plans and Sections Within the Architectural, Engineering and Construction (AEC) industry [C] ∥Intelligent and Informed-Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2019. 2019.

UZUN C, ÇOLAKOgLU M B. Architectural Drawing Recognition A Case Study for Training the Learning Algorithm With Architectural Plan and Section Drawing Images[C] ∥2020.

SHARMA D, GUPTA N, CHATTOPADHYAY C, et al. DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans[C] ∥Proceedings of the International Conference on Document Analysis and Recognition, ICDAR. 2017.

BARD J, BIDGOLI A, CHI W W. Image Classification for Robotic Plastering with Convolutional Neural Network[G] ∥ Robotic Fabrication in Architecture, Art and Design 2018. 2019.

PENG X, LIU P, JIN Y. The Age of Intelligence: Urban Design Thinking, Method Turning and Exploration[G] ∥Proceedings of the 2019 DigitalFUTURES. 2020.

LIN B, JABI W, RONGDAN D . Urban Space Simulation Based on Wave Function Collapse and Convolutional Neural Network [C] ∥Proceedings of Simaud. 2020: 139-146.

NEWTON D. Multi-Objective Qualitative Optimization (MOQO) in Architectural Design[J] . Computing for a Better Tomorrow: Proceedings of the Education and Research in Computer Aided Architectural Design in Europe (eCAADe), Faculty of Civil Engineering, Architecture and Environmental Engineering, 2018, 1:187-196.

TURLOCK M, STEINFELD K. Necessary Tension: A Dual-Evaluation Generative Design Method for Tension Net Structures [G] ∥Impact: Design With All Senses. Cham: Springer International Publishing, 2020: 250-262.

ZHANG Y, GRIGNARD A, AUBUCHON A, et al. Machine Learning for Real-Time Urban Metrics and Design Recommendations[J] . Recalibration on Imprecision and Infidelity Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture, ACADIA 2018, 2018:196-205.

CAO R, FUKUDA T, YABUKI N. Quantifying Visual Environment by Semantic Segmentation Using Deep Learning[C] ∥Intelligent and Informed Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2019. 2019.

SHI X, WANG C, WANG M, et al. An Innovative Approach to Determine Building Window-To-Wall Ratios for Urban Energy Simulation[C] ∥Proceedings of Simaud. 2020: 57-60.

NOYMAN A, LARSON K. A Deep Image of the City: Generative Urban-Design Visualization[C] ∥Proceedings of SimAUD. 2020: 155-162.

HUANG W, ZHENG H. Architectural drawings recognition and generation through machine learning[J] . Recalibration on Imprecision and Infidelity-Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture, ACADIA 2018, 2018( 11): 156-165.

NEWTON D. Deep Generative Learning for the Generation and Analysis of Architectural Plans with Small Datasets[C] ∥2020.

CHAILLOU S. AI + Architecture. Towards a New Approach [EB/OL] . : 1-188( 2019)[2020-06-14] . http: ∥ stanislaschaillou. com/articles.html.

ROSSI G, NICHOLAS P. Re/Learning the Wheel. Methods to Utilize Neural Networks as Design Tools for Doubly Curved Metal Surfaces[C] ∥Proceedings of ACADIA. 2018: 146-155.

THOMSEN M, NICHOLAS P, TAMKE M, et al. Predicting and Steering Performance in Srchitectural Materials[C] ∥2020.

STEINFELD K. GAN Loci:Imaging Place Using Generative Adversarial Networks[C] ∥Proceedings of ACADIA. 2019: 392403.

HE WanYu. From Competition, Coexistence To Win-Win— Relationship Between Intelligent Design Tools And Human Designers[J] . Landscape Architecture Frontiers, 2019, 7(2): 76-83.

STEINFELD K, PARK K, MENGES A, et al. Fresh Eyes-A Framework for the Application of Machine Learning to Generative Architectural Design, and a Report of Activities at Smartgeometry 2018[G] ∥LEE J-H. Computer-Aided Architectural Design.《Hello, Culture》. CAAD Futures 2019. Communications in Computer and Information Science. 2019: 32-46.

MOKHTAR S, SOJKA A, DAVILA C C. Conditional Generative Adversarial Networks for Pedestrian Wind Flow Approximation [C] ∥Proceedings of SimAUD. 2020: 463-470.

DUERING S, CHRONIS A, KOENIG R. Optimizing Urban Systems: Integrated Optimization of Spatial Configurations[C] ∥ Proceedings of SimAUD. 2020: 503-509.

AZIZI V, USMAN M, PATEL S, et al. Floorplan Embedding with Latent Semantics and Human Behavior Annotations[C] ∥ Proceedings of SimAUD. 2020: 337-344.

LUO D, WANG J, XU W. Robotic Automatic Generation of Performance Model for Non-Uniform Linear Material Via Deep Learning[C] ∥CAADRIA 2018 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting. 2018.

CHEN D, LUO D, XU W, et al. Re-Perceive 3D Printing With Artificial Intelligence[C] ∥Proceedings of eCAADe 37 / SIGraDi 23. 2019: 443-450.

KAROJI G, HOTTA K, HOTTA A, et al. Pedestrian Dynamic Behaviour Modeling -An application to commercial environment using RNN framework[C] ∥Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia (Caadria 2019) . 2019: 281-290.

TOULKERIDOU V. Steps Towards AI Augmented Parametric Modeling[J] . 2018, 1: 81-90.

CAMPO M Del, MANNINGER S, CARLSON A. Hallucinating Cities-A Posthuman Design Method Based on Neural Networks [C] ∥Proceedings of SimAUD. 2020: 255-262.

DEL CAMPO M, MANNINGER S, SANCHE M, et al. The church of AI: An examination of architecture in a posthuman design ecology[C] ∥Intelligent and Informed -Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2019. 2019.

DEL CAMPO M, MANNINGER S, WANG L J, et al. Sensibilities of Artificial Intelligence[G] ∥Impact: Design With All Senses. 2020.

CAMPO M, CARLSON A. Imaginary Plans[J] . : 1-8.

CAMPO M D, MANNINGER S, CARLSON A, et al. Machine Hallucinations[J] . Iass, 2019: 2001-2012.

ÖZEL G, ENNEMOSER B. Interdisciplinary AI: A Machine Learning System for Streamlining External Aesthetic and Cultural Influences in Architecture [C] ∥ Proceedings of ACADIA. 2019: 380-391.

SUN Cheng, QU DaGang, HUANG Xi. Towards AI-Architect Interactive and Collaborative Architectural Design: A Case Study of Intelligent Stylization of Building Shape [J] . Architectural Journal, 2020(2): 74-78.

FERRANDO C, DALMASSO N, MAI J, et al. Architectural Distant Reading: Using Machine Learning to Identify Typological Traits Across Multiple Buildings[C] ∥Proceedings of the 18th International Conference on CAAD Futures. 2019: 204-217.

JABI W, ALYMANI A. Graph Machine Learning using 3D Topological Models[C] ∥Proceedings of SimAUD. 2020: 421-428.

ABDELRAHMAN M M, CHONG A, MILLER C. Build2Vec: Building Representation in Vector Space[C] ∥Proceedings of Simaud. 2020: 101-104.

KOH I, AMORIM P, HUANG J. Machinic Design Inference: From Pokmon to Archiitecture A Probabilistic Machine Learning Model for Generative Design using Game Levels Abstractions [C] ∥Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia ( Caadria 2019) . 2019: 421-430.

HOSMER T, TIGAS P. Deep Reinforcement Learning for Autonomous Robotic Tensegrity[C] ∥ACADIA ∥ 2019 Ubiquity and Autonomy. Paper Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture. 2019.

NGUYEN A T, REITER S, RIGO P. A Review on SimulationBased Optimization Methods Applied to Building Performance Analysis [J] . Applied Energy, Elsevier Ltd, 2014, 113: 10431058.

EISENHOWER B, O ’NEILL Z, NARAYANAN S, et al. A Methodology for Meta-Model Based Optimization in Building Energy Models[J] . Energy and Buildings, 2012, 47: 292-301.

WANG T C, LIU M Y, ZHU J Y, et al. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs [C] ∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2018.

COSTA A, NANNICINI G. RBFOpt: an Open-Source Library for Black-Box Optimization With Costly Function Evaluations [J] . Mathematical Programming Computation, 2018.

WORTMANN T. OPOSSUM: Introducing and Evaluating a Model-based Optimization Tool for Grasshopper[J] . Proceedings of the CAADRIA 17, 2017.

RIVERO D, DORADO J, RABUÑAL J R, et al. Modifying Genetic Programming for Artificial Neural network Development for Data Mining[J] . Soft Computing, 2009.

https: ∥ www. sidefx. com/tutorials/machine-learning-datapreparation/[EB/OL] .




DOI: https://doi.org/10.33142/jsa.v1i4.14759

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Copyright (c) 2024 Chenlong MA, Shuyan ZHU, Mingjie WANG

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ISSN: 3029-2336 | Jointly published by Viser Technology Pte. Ltd. and Editorial Department of Southern Architecture