Buildings and Semantics: Data Models and Web Technologies for the Built Environment

✍ Scribed by Pieter Pauwels, Kris McGlinn

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 329
• Category: Library

✦ Synopsis

The built environment has been digitizing rapidly and is now transforming into a physical world that is at all times supplemented by a fully web-supported and interconnected digital version, often referred to as Digital Twin. This book shows how diverse data models and web technologies can be created and used for the built environment. Key features of this book are its technical nature and technical detail. The first part of the book highlights a large diversity of IT techniques and their use in the AEC domain, from JSON to XML to EXPRESS to RDF/OWL, for modelling geometry, products, properties, sensor and energy data. The second part of the book focuses on diverse software solutions and approaches, including digital twins, federated data storage on the web, IoT, cloud computing, and smart cities. Key research and strategic development opportunities are comprehensively discussed for distributed web-based building data management, IoT integration and cloud computing. This book aims to serve as a guide and reference for experts and professionals in AEC computing and digital construction including Master's students, PhD researchers, and junior to senior IT-oriented AEC professionals.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Figures
Tables
About the authors
Contributors
Foreword
Preface
Acronyms
Part I: Semantics and data
1 Building product models, terminologies, and object type libraries
1.1 Introduction
1.1.1 A brief history of CAD/BIM
1.1.2 Tackling CAD/BIM data exchange
1.1.3 Seamless data exchange: the endemic problem
1.2 Concepts and definitions
1.2.1 Chapter definitions
1.3 Structured vocabularies
1.3.1 Structured vocabulary types
1.3.1.1 Classification systems
1.3.1.2 Taxonomy
1.3.1.3 Ontology
1.3.1.4 Data dictionary
1.3.1.5 Object-type library
1.3.2 Functionality and features
1.3.2.1 Object-oriented functionality
1.3.2.2 Semantics and logic
1.4 Digital building exchange formats and schemas
1.4.1 Semantic web and linked data
1.4.1.1 Resource description framework (RDF)
1.4.1.2 Web ontology language (OWL)
1.4.1.3 Simple knowledge organisation system (SKOS)
1.4.2 ISOs for building classifications
1.5 Methods and techniques
1.5.1 Product and solid modelling techniques
1.5.2 Information collection mechanisms
1.5.3 Development and management
1.6 Practical examples in the AECO industry
1.6.1 Core vocabularies and linked datasets
1.6.2 Existing AECO ontologies
1.6.3 Existing OTLs and data dictionaries
1.7 Open research challenges
1.7.1 System limitations
1.7.2 Open standard limitations
1.8 Conclusion
Notes
2 Property modelling in the AECO industry
2.1 Introduction
2.1.1 Simple property names and values
2.1.2 More complex property names and values with metadata included
2.2 Guidelines and state of practice for modelling and exchanging properties
2.2.1 Definition of properties
2.2.1.1 Entity relationship diagrams (ERD)
2.2.1.2 UML class diagrams
2.2.2 Application and use of defined properties
2.3 Property modelling approaches
2.3.1 Simplified property modelling
2.3.1.1 Advantages
2.3.1.2 Disadvantages
2.3.1.3 Primary scenarios of use
2.3.1.4 Requirements
2.3.1.5 IFC-SPFF example
2.3.2 Complex property modelling
2.3.2.1 Advantages
2.3.2.2 Disadvantages
2.3.2.3 Primary scenarios of use
2.3.2.4 Requirements
2.3.2.5 IFC-SPFF example
2.4 Asserting properties in a semantic web context
2.4.1 Methods to attach properties
2.4.2 Units for quantitative properties
2.5 Graph patterns for property modelling
2.5.1 Level 1
2.5.2 Level 2
2.5.3 Level 3
2.5.4 Summary
2.6 Property definitions for usage in a semantic web context
2.6.1 Approach 1: hierarchy of rdf:Property
2.6.2 Approach 2: hierarchy of owl:AnnotationProperty
2.6.3 Approach 3: hierarchy of owl:DatatypeProperty and owl:ObjectProperty
2.6.4 Approach 4: hierarchy of owl:Class
2.6.5 Approach 5: hierarchy of skos:Concept
2.7 Towards a recommended modelling of properties
2.7.1 Available implementations
2.7.2 Recommendations
2.8 Conclusion
Acknowledgements
Notes
3 Web technologies for sensor and energy data models
3.1 Introduction
3.2 Model-based approaches to assessing the energy performance of buildings
3.2.1 Analysis and prediction of the energy performance of buildings
3.2.2 Monitoring and sensor data
3.2.3 Access and use of energy data
3.2.4 Energy analysis in BIM-based projects
3.2.5 Energy performance certification
3.3 Energy data models
3.3.1 System approach definition
3.3.2 Energy modelling of buildings and cities
3.3.3 Standards
3.3.4 Ontologies
3.3.4.1 Ontologies in the construction sector
3.3.4.2 Ontologies in the energy domain
3.3.4.3 Ontologies and sensors
3.3.5 Research projects
3.4 Enabling technologies for sensor data-based applications
3.4.1 Building sensor data and technologies
3.4.2 Storing and accessing building sensor data
3.5 Conclusions
Notes
4 Geometry and geospatial data on the web
4.1 Introduction
4.2 Geometry and geospatial data
4.2.1 Terminology
4.2.2 Importance of geometry and geospatial data to AEC
4.2.2.1 Integration in traditional BIM
4.2.2.2 Challenges in traditional BIM
4.3 Integrating geometry and geospatial data in a web context
4.3.1 Approach 1: RDF-based geometry descriptions
4.3.1.1 Lists in RDF
4.3.2 Approach 2: JSON-LD for web geometry
4.3.3 Approach 3: Non-RDF geometry as RDF literals
4.3.4 Approach 4: Linking to Non-RDF geometry files
4.3.5 Multiple geometry descriptions
4.4 Existing implementations for integration of graphs
4.4.1 Ontology for managing geometry (OMG)
4.4.1.1 Level 1: referencing geometry descriptions in a semantic web context
4.4.1.2 Level 2: handling multiple geometry descriptions
4.4.1.3 Level 3: versioning geometry descriptions
4.4.1.4 Explicit and implicit dependencies
4.4.1.5 Summary
4.4.2 File ontology for geometry formats (FOG)
4.4.3 Geometry metadata ontology (GOM)
4.4.4 Summary
4.5 Tools for integrating geometry and geospatial data
4.5.1 Spatial querying
4.5.1.1 GeoSPARQL
4.5.1.2 stSPARQL
4.5.1.3 BimSPARQL
4.5.1.4 Geospatial geometric literals
4.5.2 Transforming and viewing geometry
4.5.2.1 LBDserver
4.5.2.2 Visualising heterogeneous geometry descriptions
4.5.2.3 Data service for RDF-based geometry descriptions
4.5.2.4 Integrating geospatial data and building data
4.5.3 Conversion of geospatial data to industry foundation classes
4.5.4 Conversion of industry foundation classes to geospatial data
4.5.4.1 Interlinking geospatial building data with DBpedia data
4.5.4.2 Applications to support querying of interlinked geospatial data
4.6 Conclusion
Notes
5 Open data standards and BIM on the cloud
5.1 Introduction
5.1.1 Building data interoperability
5.1.2 Data exchange
5.1.3 Standardisation versus flexibility
5.2 IFC: the leading standard for BIM data
5.2.1 IFC data model
5.2.2 Modularity in IFC
5.2.3 Partial exchanges in IFC
5.3 How to move the data to the cloud?
5.3.1 XML
5.3.1.1 XML from IfcDoc
5.3.1.2 XML from IFC.JAVA class library
5.3.1.3 XML from Autodesk Revit
5.3.2 JSON
5.3.3 RDF
5.4 Data modelling Approach 1: backwards compatible file transformations and data exchanges
5.4.1 Full file serialisations
5.4.2 File size
5.4.3 Round-tripping
5.4.4 File metadata
5.4.5 Inverse relationships
5.4.6 Polymorphism
5.4.7 Internal and external referencing
5.4.8 Exchange processes
5.5 Data modelling Approach 2: forward towards online data linking
5.5.1 Modular snippets
5.5.2 Web services and microservices
5.5.3 What about the 2D and 3D geometry?
5.5.4 Exchange processes: open APIs and CDEs
5.6 Data modelling Approach 3: JSON-LD
5.6.1 What is JSON-LD?
5.6.2 Standardisation inside the JSON specification of data
5.6.3 Unique referencing using URIs
5.6.4 Inverse relationships and polymorphism
5.6.5 Exchange processes and the use of framing
5.7 Example applications and consuming web services
5.7.1 Convertors, translators and transmuters
5.7.2 Rhino and grasshopper scripting
5.7.3 JSONPath-enabled queries
5.8 Conclusion: challenges for the future
5.8.1 A taxonomy of data representation characteristics
5.8.1.1 Encoding
5.8.1.2 Concepts
5.8.1.3 Terminology
5.8.1.4 Structure
5.8.2 Flexibility and standardisation
5.8.3 Towards service-oriented and web-based data handling architectures
Notes
Part II: Algorithms and applications
6 Federated data storage for the AEC industry
6.1 Introduction
6.2 Towards web-based construction projects
6.2.1 Connecting to open datasets
6.2.2 Automatic compliance checking
6.2.3 Relating project specifications to products on the market
6.2.4 Automatic revision of the federated model
6.2.5 Managing on-site data streams
6.3 Integrating contextual data and microservices
6.3.1 Web APIs
6.3.1.1 JSON and JSON-LD
6.3.1.2 API architectures
6.3.2 Consuming data on the web
6.3.3 Microservices
6.4 Existing environments
6.4.1 Non-specialised environments
6.4.2 AEC-specific environments
6.5 Containerisation of heterogeneous datasets in construction
6.5.1 Existing specifications on information containers
6.5.1.1 BS 1192:2007 and PAS 1192-2:2013
6.5.1.2 ISO 19650-1/2:2018
6.5.1.3 DIN SPEC 91391-2:2019
6.5.1.4 Linked Data Platform
6.5.2 ISO 21597: Information Container for linked Document Delivery (ICDD)
6.5.2.1 ICDD in a CDE
6.5.2.2 Limitations and future work
6.6 Federated project data
6.6.1 Decentral identity verification
6.6.2 Federated data storage, authentication and authorisation
6.7 Collaboration structures for the future
6.7.1 The stakeholder network
6.7.2 The project management graph
6.8 Conclusion
Notes
7 Web-based computing for the AEC industry:
overview and applications
7.1 Introduction
7.2 Cloud computing
7.3 Web-based computing tools in the AEC industry
7.3.1 Opportunities
7.3.2 Challenges
7.4 Use cases and scenarios
7.4.1 Web-enabling by wrapping
7.4.1.1 Transforming legacy data and code
7.4.1.2 Virtualisation
7.4.1.3 Containers and wrapping in the EnergyMatching platform
7.4.1.4 Platform architecture
7.4.2 Mashups
7.4.2.1 Construction project quick view platform
7.4.2.2 Extensibility
7.5 Conclusion
Notes
8 Digital twins for the built environment
8.1 Introduction
8.1.1 The digital twin concept
8.1.2 Related concepts in research
8.1.3 Landscape of nearby engineering domains
8.2 Requirements, technologies and abilities
8.2.1 Digital twin requirements
8.2.2 Digital twin technologies & abilities
8.2.3 Digital twin levels
8.2.4 Standards for digital twins
8.3 Domains within the built environment
8.3.1 Digital twin implementation examples
8.3.2 Digital twin development initiatives
8.4 A reference framework
8.4.1 Conceptual system architecture
8.4.2 The role of semantics
8.5 The future of digital twins
Notes
9 The building as a platform: predictive digital twinning
9.1 Introduction
9.1.1 Digital twins and intelligent buildings
9.1.2 Digital twinning
9.2 Background
9.2.1 The challenges
9.2.2 The potentials
9.2.3 Machine learning and advanced sensing
9.3 The University of Toronto intelligent buildings digital twin project
9.3.1 Phases 1 and 2
9.3.1.1 IBDT for UofT facilities and services
9.3.1.2 IBDT for building stakeholders
9.3.2 Phase 3
9.3.3 Project outcomes
9.4 Summary and conclusion
Note
10 IoT and edge computing in the construction site
10.1 Introduction
10.2 Construction industry: push-pull to construction 4.0 and IoT
10.3 IoT system framework
10.3.1 Edge computing
10.3.2 IoT and edge computing enabled intelligent job site
10.4 IoT connectivity and requirements
10.4.1 Networking: 6LowPAN, RPL, and LoRaWAN
10.4.2 Identification
10.4.3 Communications
10.4.4 Discovery
10.4.5 Data protocols
10.5 IoT system network management
10.6 Gaps
10.6.1 Lack of evaluation and performance metrics for IoT technology in the construction environment
10.6.2 No valuation assessment tools of the implementation of IoT-based solutions in construction
10.6.3 No standards for IoT deployment in construction
10.6.4 No “one-fits-all” IoT solution for construction
10.7 Outlook and conclusion
Notes
11 Smart cities and buildings
11.1 Introduction
11.2 Smart cities
11.2.1 Features of a smart city
11.2.2 Smart buildings and homes
11.2.2.1 Definitions and terms
11.2.2.2 An interconnected smart environment
11.2.2.3 Smart building architectures
11.2.3 Smart energy systems and grids
11.2.4 Smart mobility and transport
11.3 Digital transformation in smart cities
11.4 Data infrastructure and open data initiatives in smart cities
11.4.1 Use case: Bonn
11.4.2 Use case: Dublin
11.4.3 Use case: Toronto
11.4.4 Use case: Singapore
11.4.5 Use case: Tokyo
11.5 Linked data for smart cities
11.5.1 Implementation of linked data in smart city projects
11.5.2 Linked data for smart energy system
11.6 Data analytics approaches
11.6.1 Descriptive analytics
11.6.2 Diagnostics analytics
11.6.3 Predictive analytics
11.6.4 Prescriptive analytics overview
11.7 Conclusion
11.8 Acknowledgement
Notes
Bibliography
Index