Proceedings of the 2020 DigitalFUTURES : The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020).

Intro -- Preface -- Committees -- Honorary Advisors -- Organization Committees -- Scientific Committees (list by surname) -- Contents -- Machine Thinking -- Machinic Phylum and Architecture -- 1 Nips and Bites -- 2 Ducks and Rabbits -- References -- Pipes of AI - Machine Learning Assisted 3D Modeling Design -- 1 Principle of CNN -- 1.1 Principle and Applications of Style Transfer -- 1.2 Project Goal -- 2 2D Image Representation of 3D Volume -- 2.1 The Effect of Style Weight in Style Transfer -- 2.2 Transformation of Image to Geometry -- 2.3 Algorithm Analysis of Geometry Generation Between Adjacent Layers -- 3 Result of Section Plans -- 3.1 Result of Perspective View -- 4 Conclusion -- References -- Developing a Digital Interactive Fabrication Process in Co-existing Environment -- 1 Introduction -- 2 Related Work -- 2.1 Fabrication Process of Maker -- 2.2 Towards Co-existing Environment -- 2.3 Automation Digital Fabrication Tools -- 2.4 Summary -- 3 Methodology -- 4 The Experiment -- 5 Conclusions -- References -- Real-Time Defect Recognition and Optimized Decision Making for Structural Timber Jointing -- 1 Introduction -- 2 Defect Recognition and Removal -- 2.1 Pre-process the Image for Segmentation -- 2.2 Preparation of the Classifier -- 2.3 Preparation of the Classifier -- 3 Decision Making for Joining Timber Segments -- 4 User Interface -- 5 Discussion and Future Development -- 6 Conclusion -- References -- On-Site BIM-Enabled Augmented Reality for Construction -- 1 Introduction -- 1.1 Motivation -- 1.2 Related Work -- 1.3 Our Solution -- 2 AR Application -- 2.1 Model Overlay Using Augmented Reality -- 2.2 Model Interaction as Query System -- 2.3 Abstraction of Drawings -- 2.4 Additional Features -- 3 Data Pipeline -- 3.1 BIM Pre-processing, Custom Parameter Creation and Population.

3.2 Construction Document Export and Metadata Post-processing -- 4 Unity Reflect -- 5 Results -- 5.1 Case Study/User Testing -- 5.2 Future Development -- 5.3 Connections -- References -- Recycling Construction Waste Material with the Use of AR -- 1 Introduction -- 2 Aims -- 3 Method -- 3.1 Method 1 | Mass Customisation and Working to a Fixed Digital Model -- 3.2 Mass Customized Aggregation Geometry -- 3.3 Holographic Part Nesting -- 3.4 Mixed Reality Interface -- 3.5 Jointing -- 3.6 Fabrication and Fixing Methods -- 3.7 Method 2 | Working to a Flexible Digital Model -- 3.8 Results -- 4 Discussion -- 4.1 Future Development -- References -- Growing Shapes with a Generalised Model from Neural Correlates of Visual Discrimination -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Conclusion -- References -- Cyborgian Approach of Eco-interaction Design Based on Machine Intelligence and Embodied Experience -- 1 Tracing Cyborgian Theory and Embodied Cognition -- 1.1 A Hybrid of Part Clock Part Swarm [10] -- 1.2 The Importance of the Presence and the Bodily Experience -- 2 How Cyborgian Approach Activates Plants? -- 2.1 How They Sense -- 2.2 How They Think -- 2.3 How They Actuate -- 3 How Cyborgian Approach Encourages Human Participation? -- 3.1 Experience Level -- 3.2 Experience Assessment -- 4 Design an Interactive Outdoor Environment -- 4.1 Challenges and Opportunities of Outdoor Interaction -- 4.2 A Cyborgian Eco-interaction Design Model -- 5 Conclusion and Outlook -- References -- Machine Seeing -- A Large-Scale Measurement and Quantitative Analysis Method of Fa�cade Color in the Urban Street Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 2.1 Urban Color Planning -- 2.2 Fa�cade Color Measurements -- 2.3 Quantitative Analysis of Visual Quality in Urban Street -- 3 Methodology -- 3.1 Study Area and Workflow -- 3.2 Street View Data Acquisition.

3.3 Building Fa�cade Segmentation and Data Cleaning -- 3.4 Fa�cade Color Calculation -- 4 Results -- 5 Discussion and Conclusion -- References -- Suggestive Site Planning with Conditional GAN and Urban GIS Data -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Acquisition and Feature Engineering -- 3.2 Machine Learning -- 3.3 Visualization -- 4 Case Study: Taking Boston as Example -- 4.1 Data Acquisition and Feature Engineering -- 4.2 Model Building and Training -- 4.3 Results and Visualization -- 5 Summary -- References -- Understanding and Analyzing the Characteristics of the Third Place in Urban Design: A Methodology for Discrete and Continuous Data in Environmental Design -- 1 Background -- 2 Methodology -- 2.1 Data and Data Structure for Manipulation -- 2.2 Pixel Structure for Continuous Data and Blending Data with Neighbors -- 2.3 Graph Structure for Discrete Data -- 3 Case Study Implementation -- 3.1 Site Selection -- 3.2 Parse Third Place Data and Visualization -- 3.3 Generate Data Structures and Inspect with Visualizations -- 3.4 Comparisons and Results -- 4 Discussion -- 5 Conclusion -- 6 Future Work -- References -- Sensing the Environmental Neighborhoods -- 1 Introduction -- 1.1 Sensing Kit Design -- 1.2 Case Study -- 1.3 Summary -- References -- A Performance-Based Urban Block Generative Design Using Deep Reinforcement Learning and Computer Vision -- 1 Introduction -- 2 Methodology -- 2.1 DRL Based Generative Design Framework -- 2.2 DDPG Agent -- 2.3 Hough Transform -- 3 Case Study -- 3.1 Observation, Action and Reward -- 3.2 Site Information -- 4 Results -- 5 Conclusions and Future Work -- References -- The Development of 'Agent-Based Parametric Semiology' as Design Research Program -- 1 Theory Background -- 2 Why We Need Agent-Based Life-Process Crowd Simulation.

3 The Intelligence Upgrading of Agent-Based Crowd Simulation -- 3.1 Crowd Behaviour Pattern Analysis -- 3.2 Intelligent Agents -- 3.3 Semantic Virtual Environment -- 4 Quantitative Analysis, Evaluation, and Optimization -- 4.1 Methodology and Toolset -- 4.2 Scenario and Example -- 5 Discussion -- References -- Machine Learning -- Machine Learning Aided 2D-3D Architectural Form Finding at High Resolution -- 1 Introduction -- 2 Relative Work -- 3 Method -- 4 Results -- 4.1 Training Data Preparation -- 4.2 Main Network Training -- 4.3 Multiple Network Training -- 5 Conclusion -- References -- Exploration of Campus Layout Based on Generative Adversarial Network -- 1 Introduction -- 2 Related Work in the Field of Architectural Layout -- 3 Methods -- 4 Experimental Results and Analysis -- 5 Discussion -- Appendix -- References -- A Preliminary Study on the Formation of the General Layouts on the Northern Neighborhood Community Based on GauGAN Diversity Output Generator -- 1 Introduction -- 2 Research -- 2.1 AI Application in Architecture -- 2.2 Deep Learning Architectural Plan Generator Application -- 3 Methodology -- 3.1 GauGAN -- 3.2 Step Training -- 4 Machine Learning for the General Layout Shapes of the Northern Neighborhoods in China -- 4.1 Morphological Analysis -- 4.2 Data Conversion -- 4.3 Model Architecture -- 4.4 Vectorization and 3D Procedural Modeling -- 4.5 Experiment Result -- 5 Conclusion -- 5.1 GauGAN Is More in Line with Architectural Design Needs Than Pix2pix (Pix2pixHD) -- 5.2 The Use of Step Training Can Improve the Clarity of Generated Results and Allow the Later Vectorization to Be More Convenient -- References -- Artificial Intuitions of Generative Design: An Approach Based on Reinforcement Learning -- 1 Introduction -- 1.1 Contemporary Algorithmic Generative System -- 1.2 Artificial Intuitions -- 2 Background.

2.1 Machine Learning with Generative Design -- 2.2 Reinforcement Learning -- 3 Methodology -- 3.1 Intuitive Random Walk Formation -- 3.2 RL Actions Definition -- 3.3 RL Observations Definition -- 3.4 RL Reward Definition -- 4 Discussions -- 4.1 Training Process and Outcomes -- 4.2 Further Research -- 5 Conclusions -- References -- Collection to Creation: Playfully Interpreting the Classics with Contemporary Tools -- 1 Introduction: Generations to Generative -- 2 Process: Beyond Codified Interaction -- 3 User Analysis -- 4 Media Creation -- 5 Synthetic Text Descriptions -- 6 Thoughts -- 7 Conclusion -- References -- embedGAN: A Method to Embed Images in GAN Latent Space -- 1 Introduction -- 2 Related Work -- 2.1 Regenerating Data in GAN -- 2.2 GAN Latent Walk -- 3 Method -- 3.1 Principle -- 3.2 Architecture -- 3.3 Training Details -- 4 Application -- 5 Evaluation -- 6 Conclusion -- References -- Research on Architectural Form Optimization Method Based on Environmental Performance-Driven Design -- 1 Introduction -- 2 Performance-Driven Design and Its Advantages -- 2.1 Performance-Driven Design Theory -- 2.2 Performance-Driven Design Advantages Compared with Bionic Form Design -- 3 Performance-Driven Architectural Form Optimization Method -- 3.1 Combined with Parametric Design -- 4 Form Optimization Simulation Process Establishment -- 5 Design Practice -- 5.1 Project Background -- 5.2 Design Parameters Selection and Numerical Constraint -- 5.3 Setting Simulation Parameters -- 5.4 Form Optimization Process Diagram -- 5.5 Result Analysis -- 6 Conclusion -- References -- Optimization and Prediction of Design Variables Driven by Building Energy Performance-A Case Study of Office Building in Wuhan -- 1 Introduction -- 2 Research Method -- 2.1 Research Objectives -- 2.2 Research Method.

2.3 Multi-Island Genetic Algorithm (MIGA) and Radial Basis Functions Artificial Neural Networks (RBF-ANNs).

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.