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

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