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Digital Twins in Construction and the Built Environment, 2024
- Cover
- Half Title
- Title Page
- Copyright Page
- Contents
- Contributors
- Acknowledgments
- Preface
- Acronyms
- Chapter 1: State of the Art of Digital Twins for Built Environments [Go to Page]
- 1.1 Introduction
- 1.2 Research Methodology
- 1.3 Data Analysis and Discussion [Go to Page]
- 1.3.1 Digital Twin Conceptualization [Go to Page]
- 1.3.1.1 Definitions of Digital Twin
- 1.3.1.2 Types of Digital Twin
- 1.3.1.3 Characteristics of Digital Twin
- 1.3.1.4 Components of Digital Twin
- 1.3.1.5 Digital Twin.’.s System Architecture
- 1.3.2 A Comparison between Building Information Modeling and Digital Twin [Go to Page]
- 1.3.2.1 Data Requirements
- 1.3.2.2 Building Information Modeling Uses versus Digital Twin Enterprise Solution
- 1.3.2.3 Scales of Technology Deployment
- 1.3.3 Benefits and Challenges of Digital Twin.’.s Adoption [Go to Page]
- 1.3.3.1 Digital Twin Adoption.’.s Perceived Benefits and Opportunities
- 1.3.3.2 Digital Twin Adoption.’.s Challenges and Barriers
- 1.3.4 Strategic Planning for Digital Twin Implementation [Go to Page]
- 1.3.4.1 Digital Twin.’.s Stakeholders and Strategies
- 1.3.4.2 Digital Twin Technology Stack
- 1.3.4.3 Digital Twin.’.s Maturity Models
- 1.4 Conclusion
- Data Availability Statement
- References
- Chapter 2: Building a Bridge between Building Information Modeling and Digital Twins: Introducing Invariant Signatures of Architecture, Engineering, and Construction Objects [Go to Page]
- 2.1 Introduction and Background [Go to Page]
- 2.1.1 Building Information Modeling and Digital Twins
- 2.1.2 Interoperability
- 2.1.3 Invariant Signatures
- 2.2 Systematic Introduction of Invariant Signatures of Architecture, Engineering, and Construction Objects [Go to Page]
- 2.2.1 History
- 2.2.2 Development [Go to Page]
- 2.2.2.1 Geometric Signature
- 2.2.2.2 Locational Signature
- 2.2.2.3 Material Signature
- 2.3 Analysis of the Use of Invariant Signatures to Support Building Information Modeling Interoperability with Digital Twins [Go to Page]
- 2.3.1 Proven Successes
- 2.3.2 Life Cycle of an Architectural, Engineering, and Construction Project
- 2.3.3 Components of an Architectural, Engineering, and Construction Project
- 2.3.4 Limitations and Future Research Directions
- 2.4 Conclusions
- Data Availability Statement
- Acknowledgments
- References
- Chapter 3: Digital Twin–Enabled Health Monitoring of Construction Workers during Robotic Teleoperation [Go to Page]
- 3.1 Introduction
- 3.2 The Concept of Digital Twin for Human–Robot Interactions
- 3.3 Virtual Reality–Based Digital Twin Development
- 3.4 Methodology [Go to Page]
- 3.4.1 Overview
- 3.4.2 Worker Physiological Sensing [Go to Page]
- 3.4.2.1 Signal Processing and Classifier Training
- 3.4.2.2 Near-Real-Time Cognitive Load Assessment
- 3.4.3 Digital Twin Development [Go to Page]
- 3.4.3.1 Virtual Reality–Based Human–Robot Teaming Interface
- 3.4.3.2 Robot Teleoperation in Unity
- 3.4.4 System Performance Assessment
- 3.5 Results and Discussion
- 3.6 Summary, Conclusions, and Future Research
- Acknowledgments
- References
- Chapter 4: Digital Twin–Based Ergonomic Risk Assessment Framework for Maintenance Technicians in Near Real Time [Go to Page]
- 4.1 Introduction
- 4.2 Digital Twin–Based Ergonomic Risk Assessment Framework [Go to Page]
- 4.2.1 Optical Near-Real-Time Ergonomic Risk Assessment Method
- 4.2.2 Non-optical Near-Real-Time Ergonomic Risk Assessment Method
- 4.2.3 REBA Ergonomic Risk Assessment Tool
- 4.2.4 Non-optical Task-Based Ergonomic Risk Assessment Method
- 4.3 Discussion
- 4.4 Conclusion
- Acknowledgments
- References
- Chapter 5: From Purpose to Technology: A Requirements-Driven Approach to Designing Digital Twin Implementations [Go to Page]
- 5.1 Introduction
- 5.2 Digital Twin Implementation Design: From Purpose to Technology [Go to Page]
- 5.2.1 Digital Twin Purpose
- 5.2.2 Digital Twin Implementation Framework
- 5.2.3 Digital Twin Requirements [Go to Page]
- 5.2.3.1 Digital Twin Update Intervals
- 5.2.3.2 Digital Twin Usage
- 5.2.3.3 Digital Twin Data Integration
- 5.2.3.4 Digital Twin Data Granularity
- 5.2.4 Digital Twin Integrity
- 5.2.5 Further Aspects
- 5.3 Technological Components of Geometric–Semantic Digital Twins [Go to Page]
- 5.3.1 Data Acquisition [Go to Page]
- 5.3.1.1 Acquisition Sensors and Platforms
- 5.3.1.2 Data Transformation and Preprocessing
- 5.3.2 Data Enrichment [Go to Page]
- 5.3.2.1 Data-Driven Clustering
- 5.3.2.2 Model-based Clustering
- 5.3.2.3 Semantic Enrichment on Spatial and Visual Data
- 5.3.2.4 Semantic Enrichment on Design Data
- 5.3.3 Geometry Provision [Go to Page]
- 5.3.3.1 Implicit Representation
- 5.3.3.2 Boundary Representation
- 5.3.3.3 Procedural Modeling
- 5.3.3.4 Parametric and Feature-Based Modeling
- 5.4 Exemplary Digital Twin Implementations [Go to Page]
- 5.4.1 Use Case Example: Efficient Heating, Ventilation, and Air-Conditioning Maintenance [Go to Page]
- 5.4.1.1 Requirements
- 5.4.1.2 Digital Twin Implementation and Evaluation
- 5.4.2 Further Use Cases
- 5.4.3 Growing the Digital Twin: Marginal Cost for Extension
- 5.5 Conclusion
- Data Availability Statement
- References
- Chapter 6: Defining a System Architecture for Operational Digital Twins for Predictive Maintenance [Go to Page]
- 6.1 Introduction
- 6.2 Digital Transformation in Maintenance Management
- 6.3 Predictive Maintenance for Buildings [Go to Page]
- 6.3.1 Predictive Maintenance Techniques—Applications of Predictive Maintenance in Maintenance [Go to Page]
- 6.3.1.1 Vibration Monitoring and Analysis
- 6.3.1.2 Thermography
- 6.3.1.3 Process Parameters
- 6.3.2 Sensors for Condition Monitoring
- 6.3.3 Implementing Predictive Maintenance
- 6.3.4 Fault Detection and Diagnosis [Go to Page]
- 6.3.4.1 Classification Methods
- 6.3.4.2 Reasoning or Inference Methods
- 6.4 Digital Twins [Go to Page]
- 6.4.1 Cyber-Physical Systems and Digital Twins
- 6.4.2 Defining Digital Twins for Predictive Maintenance
- 6.4.3 Designing and Implementing Digital Twins for Predictive Maintenance
- 6.4.4 System Architecture
- 6.4.5 Testing Design and Implementation of Digital Twins
- 6.5 Conclusion and Future Outlook
- References
- Chapter 7: Digital Twin Applications for Building Energy and Carbon Performance [Go to Page]
- 7.1 Introduction [Go to Page]
- 7.1.1 Understanding Building Information Modeling and Digital Twins
- 7.1.2 The Potential of Digital Twin Technologies
- 7.1.3 Data Requirements for Digital Twins
- 7.1.4 Internet of Things Developments and Energy Savings
- 7.1.5 Digital Twin Technology and Energy Savings
- 7.2 Digital Twin Implementation Framework
- 7.3 Digital Twins for an Energy-Saving Office Building [Go to Page]
- 7.3.1 Case Study Description
- 7.3.2 Implementation of Internet of Things in the Building
- 7.3.3 Energy Savings and Carbon Emission Reductions under Four Scenarios [Go to Page]
- 7.3.3.1 Total System Energy Result—Conventional Building
- 7.3.3.2 Total System Energy Result—Daylight Harvesting Model
- 7.3.3.3 Total System Energy Result – Apache Heating, Ventilation, and Air-Conditioning Model
- 7.3.3.4 Total System Energy Result—Smart Building
- 7.3.3.5 Carbon Emission—Smart versus Conventional Building
- 7.3.4 Discussion
- 7.4 Conclusion
- References
- Chapter 8: Predictive Maintenance of Building Facility: A Digital Twin Framework Using Long Short-Term Memory Encode–Decode Model [Go to Page]
- 8.1 Introduction
- 8.2 Background Studies [Go to Page]
- 8.2.1 Data-driven Approaches for PdM of Building Facilities
- 8.2.2 Potential of Digital Twins in Predictive Maintenance
- 8.2.3 Lack of Standardization in Digital Twins Model Frameworks
- 8.3 Knowledge Gap and Research Objective
- 8.4 Proposed Framework [Go to Page]
- 8.4.1 Data Collection
- 8.4.2 Model Development
- 8.4.3 Failure Prediction and System Monitoring
- 8.5 Results and Discussion
- 8.6 Innovation
- 8.7 Conclusion
- Acknowledgments
- References
- CHAPTER 9: Digital Twin Development for a Smart Port: A Case Study of Weihai Port [Go to Page]
- 9.1 Introduction
- 9.2 Related Works [Go to Page]
- 9.2.1 Digital Twin Development in the Civil Infrastructure Sector
- 9.2.2 Digital Twin Development Specifically in Port Sector Worldwide
- 9.2.3 Digital Twin Development in China
- 9.2.4 Challenges to Design and Develop Port Digital Twin
- 9.3 Establishing Digital Twin for Weihai Port [Go to Page]
- 9.3.1 Background of Weihai Port
- 9.3.2 Business Analysis [Go to Page]
- 9.3.2.1 Requirement Category Framework Development
- 9.3.2.2 Requirement Collection
- 9.3.2.3 Requirement Analysis
- 9.3.3 Data Availability and Data Requirements [Go to Page]
- 9.3.3.1 Data Availability
- 9.3.3.2 Data Requirements
- 9.3.4 Multilayered Digital Twin Architecture and Data Environment [Go to Page]
- 9.3.4.1 Multilayered Digital Twin Architecture
- 9.3.4.2 Virtual Environment
- 9.3.4.3 Business Data Environment
- 9.3.5 Developed Digital Twin Modules
- 9.3.6 Production Scheduling Module
- 9.3.7 Safety and Security Module
- 9.3.8 Supplying Chain Management Module
- 9.3.9 Client Services Module
- 9.3.10 Lesson Learned [Go to Page]
- 9.3.10.1 Digital Twin Requirements and Stakeholders
- 9.3.10.2 Data Availability and Port Data Requirements
- 9.3.10.3 Data Management and Applications
- 9.4 Conclusion
- Data Availability
- Acknowledgments
- References
- Chapter 10: Digital Twins for Predictive Maintenance, Conservation, and Rehabilitation Procedures of Historical Buildings: The Case of the “Museu Republicano Convenção de Itu,” Brazil [Go to Page]
- 10.1 Introduction
- 10.2 Methods
- 10.3 From Historic Building Information Modeling Models to Digital Twins
- 10.4 Heritage Building Data Inventory
- 10.5 Scan-to-(Historic) Building Information Modeling Process: Reality Capture and Modeling [Go to Page]
- 10.5.1 Workflow Proposal
- 10.5.2 Application Study: Museu Republicano “Convenção de Itu” [Go to Page]
- 10.5.2.1 Geometric Data Acquisition and Processing
- 10.5.2.2 Improving the Accuracy of Parametric Modeling for Historic Buildings
- 10.5.2.3 Building Defect Assessment
- 10.5.2.4 Monitoring a Heritage Building
- 10.6 Discussion
- 10.7 Conclusions
- Acknowledgments
- References
- Chapter 11: Digital Twin for Bridges and Structures: Practical Applications and Challenges [Go to Page]
- 11.1 Introduction
- 11.2 Digital Twin Integration and Maturity
- 11.3 Practical Applications
- 11.4 Challenges and Considerations [Go to Page]
- 11.4.1 Practicality of Integrating All Physical Object Data
- 11.4.2 Sensor Compatibility and Integration
- 11.4.3 Connection and Remote Sensing
- 11.4.4 Data Collection
- 11.4.5 Data Interoperability
- 11.4.6 High-Performance Computing
- 11.4.7 Data Privacy and Cybersecurity
- 11.4.8 Regulation and Governance
- 11.4.9 Modernizing Current Technology
- 11.4.10 Cost-Effectiveness
- 11.4.11 Gaps in Artificial Intelligence for Optimized Maintenance
- 11.5 Conclusion
- References [Go to Page]
