On systematic approaches for interpreted information transfer of inspection data from bridge models to structural analysis

In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies

Digital-Twin based data modelling for Digital Building Logbook implementation

Construction heavily impact on the environment, thus building management plays a central role in achieving higher sustainability objectives. In the use phase, the sustainability performance needs to be balanced with safety, health and comfort requirements, ensuring that the indoor activities can be carried out in a high-performing environment. Indoor air quality is one of the main proxies for health and safety in indoor spaces and maintaining appropriate quality levels is directly impacted by built asset condition, systems operation and other contextual factors as occupancy and weather. The Digital Building Logbook is a platform enabling the interconnection of the variety of datasets used for building management. This paper introduces a framework for implementing the Digital Building Logbook utilising semantic web technologies, in the perspective of supporting better data access and knowledge extraction for Digital Twin applications in the Facilities Management domain. The proposed framework is based on the combination of Industry Foundation Classes, the BrickSchema and a custom Digital Building Logbook ontology, needed to extend the previous two. The Digital Building Logbook ontology is based on OWL and allows to structure the information on assets, maintenance interventions associated to the different spaces and systems, while IFC and BrickSchema provide a support in representing a selection of spatial and semantic building features. The developed framework aims at enhancing the data access and knowledge extraction on the building and facilitates the digital Facilities Management applications development. A validation is carried out on the Alan Reece building at the University of Cambridge and demonstrates its effectiveness in developing users' health-based maintenance prioritisation

A Semantic Offsite Construction Digital Twin- Offsite Manufacturing Production Workflow (OPW) Ontology

Offsite Manufacturing (OSM) is a modern and innovative method of construction with the potential to adopt advanced factory production system through a more structured workflow, standardised products, and the use of robotics for automation. However, there have been challenges in quantifying improvements from the conventional method, which leads to the low uptake. The concept of a digital twin (DT) is useful for OSM, which enables production to be represented virtually and visually including all activities associated with it, resources, and workflow involved. Thus, essential information in the product development process such as cost, time, waste, and environmental impacts can be assessed. However, the data required to have accurate results and better-informed decision-making come from heterogeneous data formats (i.e. spreadsheets and BIM models) and across different domains. The inclusion of semantic web technologies such as Linked Data (LD) and Web Ontology Language (OWL) models has proven to better address these challenges especially in terms of interoperability and unambiguous knowledge systematisation. Through an extensive systematic literature review followed up by a case study, an ontology knowledge structure representing the production workflow for OSM is developed. A real-life use case of a semi-automated production line of wall panel production is used to test and demonstrate the benefits of the semantic digital twin in obtaining cost and time data of the manufacturing for assessment. Results demonstrated the potential capability and power of capturing knowledge for an ontology to assess production workflow in terms of cost, time, carbon footprint thereby enabling more informed decision making for continuous improvements

Digitalization of urban multi-energy systems – Advances in digital twin applications across life-cycle phases

Urban multi-energy systems (UMES) incorporating distributed energy resources are vital to future low-carbon energy systems. These systems demand complex solutions, including increased integration of renewables, improved efficiency through electrification, and exploitation of synergies via sector coupling across multiple sectors and infrastructures. Digitalization and the Internet of Things bring new opportunities for the design-build-operate workflow of the cyber-physical urban multi-energy systems. In this context, digital twins are expected to play a crucial role in managing the intricate integration of assets, systems, and actors within urban multi-energy systems. This review explores digital twin opportunities for urban multi-energy systems by first considering the challenges of urban multi energy systems. It then reviews recent advancements in digital twin architectures, energy system data categories, semantic ontologies, and data management solutions, addressing the growing data demands and modelling complexities. Digital twins provide an objective and comprehensive information base covering the entire design, operation, decommissioning, and reuse lifecycle phases, enhancing collaborative decision-making among stakeholders. This review also highlights that future research should focus on scaling digital twins to manage the complexities of urban environments. A key challenge remains in identifying standardized ontologies for seamless data exchange and interoperability between energy systems and sectors. As the technology matures, future research is required to explore the socio-economic and regulatory implications of digital twins, ensuring that the transition to smart energy systems is both technologically sound and socially equitable. The paper concludes by making a series of recommendations on how digital twins could be implemented for urban multi energy systems

Enabling building digital twin:Ontology-based information management framework for multi-source data integration

The emergence of the digital twin concept can potentially change the way people manage built assets thoroughly. This is because the semantics-based model and linked data approach behind the digital twin, as the successor of classical BIM, provide strong capability in integrating data from fragmented and heterogeneous sources and thus enable better-informed decision-making. Taking buildings as the case, this paper demonstrates the ontology-based Information Management Framework and elaborates on the process to integrate data through a common data model. Specifically, the Foundation Data Model (FDM) representing the operation of buildings and embedded systems is developed and two patterns of integration architecture are compared. To conceptualise all the essential entities and relationships, the building topology ontology and BRICK ontology are reused and merged to serve as a feasible FDM. According to the characteristic of asset management services that digital twins support, two integration architectures are compared, including the data warehouse approach and the mediator approach. A case study is presented to elaborate on the implementation of these two approaches and their applicability. This work sets out the standardised and modularised paradigms for discovering, fetching, and integrating data from disparate sources with different data curation manners.</p

Knowledge graph-based data integration system for digital twins of built assets

The emergence of digital twin technologies offers a promising avenue for improving decision-making through the integrated use of up-to-date physical or synthetically simulated data. Nevertheless, the practical implementation of digital twins in the built environment remains a significant challenge. This paper describes a system that seamlessly integrates data into digital twins of built assets. The system uses a knowledge graph to achieve data integration, which is designed to be modular, flexible, and interoperable. The graph includes BIM models, metadata from an external IoT platform, and process-related information. The system is microservice-based and revolves around a graph database housing the knowledge graph. It employs dynamic operations to update the knowledge graph and is tested using civil engineering infrastructure examples. Results from this work can be used to create pipelines that extract and operate with data connecting computational agents integrated into the system as microservices or connected through the system API.This paper was prepared in the context of project ASHVIN. ASHVIN has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 958161. This publication reflects only author’s view and that the European Commission is not responsible for any uses that may be made of the information it contains. The first author acknowledges the funding of the FI-2021 AGAUR PhD grant.Peer ReviewedPostprint (published version

A linked-data paradigm for the integration of static and dynamic building data in digital twins

Digital Twins is an emerging field of research, mainly because they span the entire building lifecycle promising to uncover hidden inefficiencies and deliver data-driven applications. Broadly defined as real-time digital representations of physical assets, Digital Twins require a connection between static and real-time data. However, building information is usually stored in different formats across the lifecycle, making data integration a challenging task. We hereby often rely on linked data technologies, yet overall system integration approaches with multiple types of data sources. In this work, a data linking methodology is proposed to combine static building design data from Industry Foundation Classes (IFC) and dynamic data using the Brick Schema; a domain ontology which configures data analytics applications during the operational phase. To facilitate this integration, we develop a tool to facilitate the linking of building topology, product, and sensor data using the two schemata. The implementation of our methodology in a real test case demonstrates its significance in combining diverse data sources which can be an important step for the delivery of Digital Twin applications

Ontologies in Digital Twins: A Systematic Literature Review

Digital Twins (DT) facilitate monitoring and reasoning processes in cyber–physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs