scholarly journals Adoption of Blockchain Technology through Digital Twins in the Construction Industry 4.0: A PESTELS Approach

Buildings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 670
Author(s):  
Benjamin Teisserenc ◽  
Samad Sepasgozar
Keyword(s):  
Industry 4.0 ◽  
Building Blocks ◽  
Network Governance ◽  
Information Value ◽  
Information Modeling ◽  
Environmental Aspect ◽  
Technological Factors ◽  
Digital Twin ◽  
Blockchain Technology ◽  
Building Engineering

The key challenges of the building, engineering, construction, operations, and mining (BECOM) industries are the lack of trust, inefficiencies, and the fragmentation of the information value chain into vulnerable data silos throughout the lifecycle of projects. This paper aims to develop a novel conceptual model for the implementation of blockchain technology (BCT) for digital twin(s) (DT) in the BECOM industry 4.0 to improve trust, cyber security, efficiencies, information management, information sharing, and sustainability. A PESTELS approach is used to review the literature and identify the key challenges affecting BCT adoption for the BECOM industry 4.0. A review of the technical literature on BCT combined with the findings from PESTELS analysis permitted researchers to identify the key technological factors affecting BCT adoption in the industry. This allowed offering a technological framework—namely, the decentralized digital twin cycle (DDTC)—that leverages BCT to address the key technological factors and to ultimately enhance trust, security, decentralization, efficiency, traceability, and transparency of information throughout projects’ lifecycles. The study also identifies the gaps in the integration of BCT with key technologies of industry 4.0, including the internet of things (IoT), building information modeling (BIM), and DT. The framework offered addresses key technological factors and narrows key gaps around network governance, scalability, decentralization, interoperability, energy efficiency, computational requirements, and BCT integration with IoT, BIM, and DT throughout projects’ lifecycles. The model also considers the regulatory aspect and the environmental aspect, and the circular economy (CE). The theoretical framework provides key technological building blocks for industry practitioners to develop the DDTC concept further and implement it through experimental works. Finally, the paper provides an industry-specific analysis and technological approach facilitating BCT adoption through DT to address the key challenges and improve sustainability for the BECOM industry 4.0.

scholarly journals Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 151
Author(s):  
Samad M. E. Sepasgozar
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Industry 4.0 ◽  
Construction Projects ◽  
Data Exchange ◽  
Smart Cities ◽  
Information Modeling ◽  
Digital Data ◽  
Digital Twin ◽  
Industrial Sectors

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

scholarly journals Using Digital Twin Technology in Engineering Education – Course Concept to Explore Benefits and Barriers

2020 ◽  
Vol 10 (1) ◽  
pp. 377-385 ◽  
Author(s):  
Antti Liljaniemi ◽  
Heikki Paavilainen
Keyword(s):  
Engineering Education ◽  
Industry 4.0 ◽  
Digital Technologies ◽  
Digital Twin ◽  
New Knowledge

AbstractDigital Twin (DT) technology is an essential technology related to the Industry 4.0. In engineering education, it is important that the curricula are kept up-to-date. By adopting new digital technologies, such as DT, we can provide new knowledge for students, teachers, and companies. The main aim of this research was to create a course concept to research benefits and barriers of DT technology in engineering education. The research confirmed earlier findings concerning digitalization in engineering education. DT technology can increase motivation for studying and improve learning when applied correctly.

scholarly journals Robot Digital Twin towards Industry 4.0

2020 ◽  
Vol 53 (2) ◽  
pp. 10867-10872
Author(s):  
Luige Vlădăreanu ◽  
Alexandru I. Gal ◽  
Octavian D. Melinte ◽  
Victor Vlădăreanu ◽  
Mihaiela Iliescu ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Digital Twin

scholarly journals BIM-Based Digital Twin and XR Devices to Improve Maintenance Procedures in Smart Buildings: A Literature Review

2021 ◽  
Vol 11 (15) ◽  
pp. 6810
Author(s):  
Corentin Coupry ◽  
Sylvain Noblecourt ◽  
Paul Richard ◽  
David Baudry ◽  
David Bigaud
Keyword(s):  
Academic Research ◽  
Facility Management ◽  
Information Modeling ◽  
Application Framework ◽  
Remote Collaboration ◽  
Smart Buildings ◽  
Digital Twin ◽  
Industrial Sectors ◽  
M Phase ◽  
Building Information

In recent years, the use of digital twins (DT) to improve maintenance procedures has increased in various industrial sectors (e.g., manufacturing, energy industry, aerospace) but is more limited in the construction industry. However, the operation and maintenance (O&M) phase of a building’s life cycle is the most expensive. Smart buildings already use BIM (Building Information Modeling) for facility management, but they lack the predictive capabilities of DT. On the other hand, the use of extended reality (XR) technologies to improve maintenance operations has been a major topic of academic research in recent years, both through data display and remote collaboration. In this context, this paper focuses on reviewing projects using a combination of these technologies to improve maintenance operations in smart buildings. This review uses a combination of at least three of the terms “Digital Twin”, “Maintenance”, “BIM” and “Extended Reality”. Results show how a BIM can be used to create a DT and how this DT use combined with XR technologies can improve maintenance operations in a smart building. This paper also highlights the challenges for the correct implementation of a BIM-based DT combined with XR devices. An example of use is also proposed using a diagram of the possible interactions between the user, the DT and the application framework during maintenance operations.

Blockchain technology applications for Industry 4.0: A literature-based review

2021 ◽  
pp. 100027
Author(s):  
Mohd Javaid ◽  
Abid Haleem ◽  
Ravi Pratap Singh ◽  
Shahbaz Khan ◽  
Rajiv Suman
Keyword(s):  
Industry 4.0 ◽  
Blockchain Technology ◽  
Technology Applications

Digital Twins: Review and Challenges

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Maria G. Juarez ◽  
Vicente J. Botti ◽  
Adriana S. Giret
Keyword(s):  
Computer Science ◽  
Industry 4.0 ◽  
Multi Agent System ◽  
Agent System ◽  
Digital Twin ◽  
Digital Twins ◽  
Science Approach ◽  
Multi Agent ◽  
Current Operating

Abstract With the arises of Industry 4.0, numerous concepts have emerged; one of the main concepts is the digital twin (DT). DT is being widely used nowadays, however, as there are several uses in the existing literature; the understanding of the concept and its functioning can be diffuse. The main goal of this paper is to provide a review of the existing literature to clarify the concept, operation, and main characteristics of DT, to introduce the most current operating, communication, and usage trends related to this technology, and to present the performance of the synergy between DT and multi-agent system (MAS) technologies through a computer science approach.

The Building Blocks of Network Governance: Rules, Norms and Culture

2021 ◽  
Vol 2021 (1) ◽  
pp. 13722
Author(s):  
Lisa Bernstein ◽  
Simon Friis ◽  
Matthew Yeaton
Keyword(s):  
Building Blocks ◽  
Network Governance

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

2021 ◽  
Author(s):  
Senthil Krishnababu ◽  
Omar Valero ◽  
Roger Wells
Keyword(s):  
Gas Turbine ◽  
Data Storage ◽  
Gas Turbines ◽  
Building Blocks ◽  
Data Driven ◽  
High Fidelity ◽  
Test Field ◽  
Operating Characteristics ◽  
Digital Twin ◽  
The Impact

Abstract Data driven technologies are revolutionising the engineering sector by providing new ways of performing day to day tasks through the life cycle of a product as it progresses through manufacture, to build, qualification test, field operation and maintenance. Significant increase in data transfer speeds combined with cost effective data storage, and ever-increasing computational power provide the building blocks that enable companies to adopt data driven technologies such as data analytics, IOT and machine learning. Improved business operational efficiency and more responsive customer support provide the incentives for business investment. Digital twins, that leverages these technologies in their various forms to converge physics and data driven models, are therefore being widely adopted. A high-fidelity multi-physics digital twin, HFDT, that digitally replicates a gas turbine as it is built based on part and build data using advanced component and assembly models is introduced. The HFDT, among other benefits enables data driven assessments to be carried out during manufacture and assembly for each turbine allowing these processes to be optimised and the impact of variability or process change to be readily evaluated. On delivery of the turbine and its associated HFDT to the service support team the HFDT supports the evaluation of in-service performance deteriorations, the impact of field interventions and repair and the changes in operating characteristics resulting from overhaul and turbine upgrade. Thus, creating a cradle to grave physics and data driven twin of the gas turbine asset. In this paper, one branch of HFDT using a power turbine module is firstly presented. This involves simultaneous modelling of gas path and solid using high fidelity CFD and FEA which converts the cold geometry to hot running conditions to assess the impact of various manufacturing and build variabilities. It is shown this process can be executed within reasonable time frames enabling creation of HFDT for each turbine during manufacture and assembly and for this to be transferred to the service team for deployment during field operations. Following this, it is shown how data driven technologies are used in conjunction with the HFDT to improve predictions of engine performance from early build information. The example shown, shows how a higher degree of confidence is achieved through the development of an artificial neural network of the compressor tip gap feature and its effect on overall compressor efficiency.

scholarly journals Model Quality Assessment Method Based on Support Vector Machine

2021 ◽  
Vol 25 (1) ◽  
pp. 35-39
Author(s):  
Łukasz Glodek ◽  
Szymon Bysko ◽  
Witold Nocoń
Keyword(s):  
Support Vector Machine ◽  
Quality Assessment ◽  
Industry 4.0 ◽  
Assessment Method ◽  
Support Vector ◽  
Model Quality ◽  
Digital Twin ◽  
Model Quality Assessment ◽  
Virtual Commissioning ◽  
Quality Assessment Method

This paper proposes a model quality assessment method based on Support Vector Machine, which can be used to develop a digital twin. This work is strongly connected with Industry 4.0, in which the main idea is to integrate machines, devices, systems, and IT. One of the goals of Industry 4.0 is to introduce flexible assortment changes. Virtual commissioning can be used to create a simulation model of a plant or conduct training for maintenance engineers. On a branch of virtual commissioning is a digital twin. The digital twin is a virtual representation of a plant or a device. Thanks to the digital twin, different scenarios can be analyzed to make the testing process less complicated and less time-consuming. The goal of this work is to propose a coefficient that will take into account expert knowledge and methods used for model quality assessment (such as Normalized Root Mean Square Error – NRMSE, Maximum Error – ME). NRMSE and ME methods are commonly used for this purpose, but they have not been used simultaneously so far. Each of them takes into consideration another aspect of a model. The coefficient allows deciding whether the model can be used for digital twin appliances. Such an attitude introduces the ability to test models automatically or in a semi-automatic way.

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