Cover Image

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

Bibliographic Details
Main Author: Yuan, Philip F.
Other Authors: Yao, Jiawei., Yan, Chao., Wang, Xiang., Leach, Neil.
Format: eBook
Language:English
Published: Singapore : Springer Singapore Pte. Limited, 2021.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • 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 Façade Color in the Urban Street Using Deep Learning
  • 1 Introduction
  • 2 Literature Review
  • 2.1 Urban Color Planning
  • 2.2 Façade 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 Façade Segmentation and Data Cleaning
  • 3.4 Façade 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).

Similar Items