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

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.

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Cloud BIM Technology as a Means of Collaboration and Project Integration in Smart Cities

SAGE Open ◽

10.1177/21582440211033250 ◽

2021 ◽

Vol 11 (3) ◽

pp. 215824402110332

Author(s):

Ihuoma Onungwa ◽

Nnezi Olugu-Uduma ◽

Dennis R. Shelden

Keyword(s):

Real Time ◽

Smart City ◽

Data Exchange ◽

Smart Cities ◽

Information Modeling ◽

City Development ◽

Digital Modeling ◽

Project Integration ◽

Building Information ◽

Project Life

Building Information Modeling (BIM) was created to address the Architecture, Engineering, and Construction (AEC) industry’s lack of collaboration among consultants. Advances in cloud BIM have led to the easy exchange of data and real-time collaboration among consultants from conceptual design to the detailed construction drawing stage and through the project life cycle. This is critical in the development of smart cities. Cloud BIM also facilitates visualization of the city and data exchange for internet of things (IoT). Smart city development involves incorporating data from sensors and hardware attached to existing infrastructure. This article studies cloud BIM technology as a means of project integration in smart city development. To do this, a case study of digital modeling for the development of a smart city was done. Benefits include seamless communication, monitoring real-time progress, and visualization of files. Problems encountered include governance problems, problems preserving work sets, the integrity of drawings, and difficulty specifying coordinates on-site.

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

Applied Sciences ◽

10.3390/app11156810 ◽

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.

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Digital Twin-Driven Condition-Based Maintenance

10.4018/978-1-7998-7943-5.ch005 ◽

2022 ◽

pp. 109-136

Author(s):

Adolfo Crespo del Castillo ◽

Marco Macchi ◽

Laura Cattaneo

Keyword(s):

Real Time ◽

Industry 4.0 ◽

Simulation Models ◽

Condition Based Maintenance ◽

Industrial Management ◽

Digital Twin ◽

Operation And Maintenance ◽

Twin Models ◽

Technical Professionals ◽

Case Development

The world is witnessing an all-level digitalization that guides the industry and business to a restructuration in order to adapt to the new requirements of the surrounding environment. That change also concerns the labour of the technical professionals and their formation. As a consequence of this deep consciousness-raising, this chapter will investigate and develop simulation models based on the current digitalization. The aim of this chapter is the exposition of a real case development of “digital twin” models framed as part of the condition-based maintenance paradigm to improve real-time assets operation and maintenance. This model contributes by providing real-time results that could turn into a basis for the industrial management decisions and place them in the Industry 4.0 paradigm environment.

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An Internet of Things Wireless Sensor Network Data Exchange Model Based on Hierarchical Address Automatic Configuration and Header Compression Encoding Strategy

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v13i07.7374 ◽

2017 ◽

Vol 13 (07) ◽

pp. 140 ◽

Cited By ~ 1

Author(s):

Yuankun Yang ◽

Yongqing Ji

Keyword(s):

Energy Consumption ◽

Wireless Sensor Network ◽

Internet Of Things ◽

Real Time ◽

Sensor Network ◽

Data Exchange ◽

Wireless Sensor ◽

The Internet ◽

Network Data ◽

Sink Node

To explore the wireless sensor network data exchange model, an addressing strategy is applied to the Internet of Things, and the real-time communication between the underlying wireless sensor network and the Internet based on the IEEE 802.15.4 protocol is realized. In addition, Hierarchical address auto configuration strategy is adopted. First of all, inside the bottom network, it allows nodes to use link local address derived by 16-bit short address for data packet transmission. Secondly, Sink node in each underlying network accesses to the global routing prefix through the upper IP router, and combined with interface identifier, it forms Sink node global address, and realizes wireless sensor network and Internet data exchange. The research results show that the strategy has certain superiority in network cost, throughput, energy consumption and other performances. In summary, the proposed addressing strategy has the characteristics of effectively integrating heterogeneous networks, reducing system energy consumption, increasing network throughput and ensuring real-time system performance for the future Internet of things.

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Practical Use of Robot Manipulators as Intelligent Manufacturing Systems

Sensors ◽

10.3390/s18092877 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2877 ◽

Cited By ~ 1

Author(s):

Pablo Alhama Blanco ◽

Fares Abu-Dakka ◽

Mohamed Abderrahim

Keyword(s):

Real Time ◽

Industry 4.0 ◽

Manufacturing Systems ◽

Robot Manipulators ◽

Smart Cities ◽

Industrial Robot ◽

Online Communication ◽

Intelligent Manufacturing ◽

Communication Tool ◽

Industrial Big Data

This paper presents features and advanced settings for a robot manipulator controller in a fully interconnected intelligent manufacturing system. Every system is made up of different agents. As also occurs in the Internet of Things and smart cities, the big issue here is to ensure not only that implementation is key, but also that there is better common understanding among the main players. The commitment of all agents is still required to translate that understanding into practice in Industry 4.0. Mutual interactions such as machine-to-machine and man-to-machine are solved in real time with cyber physical capabilities. This paper explores intelligent manufacturing through the context of industrial robot manipulators within a Smart Factory. An online communication algorithm with proven intelligent manufacturing abilities is proposed to solve real-time interactions. The algorithm is developed to manage and control all robot parameters in real-time. The proposed tool in conjunction with the intelligent manufacturing core incorporates data from the robot manipulators into the industrial big data to manage the factory. The novelty is a communication tool that implements the Industry 4.0 standards to allow communications among the required entities in the complete system. It is achieved by the developed tool and implemented in a real robot and simulation

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Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

Sustainability ◽

10.3390/su12208629 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8629 ◽

Cited By ~ 1

Author(s):

Paula Morella ◽

María Pilar Lambán ◽

Jesús Royo ◽

Juan Carlos Sánchez ◽

Lisbeth del Carmen Ng Corrales

Keyword(s):

Supply Chain ◽

Energy Consumption ◽

Real Time ◽

Physical System ◽

Machine Tools ◽

Industry 4.0 ◽

Performance Indicator ◽

Green Supply Chain ◽

Cyber Physical System ◽

Key Performance Indicator

This work investigates Industry 4.0 technologies by developing a new key performance indicator that can determine the energy consumption of machine tools for a more sustainable supply chain. To achieve this, we integrated the machine tool indicator into a cyber–physical system for easy and real-time capturing of data. We also developed software that can turn these data into relevant information (using Python): Using this software, we were able to view machine tool activities and energy consumption in real time, which allowed us to determine the activities with greater energy burdens. As such, we were able to improve the application of Industry 4.0 in machine tools by allowing informed real-time decisions that can reduce energy consumption. In this research, a new Key Performance Indicator (KPI) was been developed and calculated in real time. This KPI can be monitored, can measure the sustainability of machining processes in a green supply chain (GSC) using Nakajima’s six big losses from the perspective of energy consumption, and is able to detect what the biggest energy loss is. This research was implemented in a cyber–physical system typical of Industry 4.0 to demonstrate its applicability in real processes. Other productivity KPIs were implemented in order to compare efficiency and sustainability, highlighting the importance of paying attention to both terms at the same time, given that the improvement of one does not imply the improvement of the other, as our results show.

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Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios

Applied Sciences ◽

10.3390/app10227964 ◽

2020 ◽

Vol 10 (22) ◽

pp. 7964

Author(s):

David Todoli-Ferrandis ◽

Javier Silvestre-Blanes ◽

Víctor Sempere-Payá ◽

Ana Planes-Martínez

Keyword(s):

Energy Consumption ◽

Low Power ◽

Packet Loss ◽

Industry 4.0 ◽

Smart Cities ◽

Data Rate ◽

Wide Area ◽

Area Network ◽

Wide Area Network ◽

Class B

Low-power wide-area network (LPWAN) technologies are becoming a widespread solution for wireless deployments in many applications, such as smart cities or Industry 4.0. However, there are still challenges to be addressed, such as energy consumption and robustness. To characterize and optimize these types of networks, the authors have developed an optimized use of the adaptative data rate (ADR) mechanism for uplink, proposed its use also for downlink based on the simulator ns-3, and then defined an industrial scenario to test and validate the proposed solution in terms of packet loss and energy.

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Digital Twin for Supply Chain Coordination in Modular Construction

Applied Sciences ◽

10.3390/app11135909 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5909

Author(s):

Dongmin Lee ◽

SangHyun Lee

Keyword(s):

Supply Chain ◽

Real Time ◽

Arrival Time ◽

Supply Chain Coordination ◽

Information Modeling ◽

Modular Construction ◽

Widespread Application ◽

Digital Twin ◽

Building Information ◽

Logistics Process

Over the past decades, the construction industry has been attracted to modular construction because of its benefits for reduced project scheduling and costs. However, schedule deviation risks in the logistics process of modular construction can derail its benefits and thus interfere with its widespread application. To address this issue, we aim to develop a digital twin framework for real-time logistics simulation, which can predict potential logistics risks and accurate module arrival time. The digital twin, a virtual replica of the physical module, updates its virtual asset based on building information modeling (BIM) in near real-time using internet of thing (IoT) sensors. Then, the virtual asset is transferred and exploited for logistics simulation in a geographic information system (GIS)-based routing application. We tested this framework in a case project where modules are manufactured at a factory, delivered to the site via a truck, and assembled onsite. The results show that potential logistical risks and accurate module arrival time can be detected via the suggested digital twin framework. This paper’s primary contribution is the development of a framework that mediates IoT, BIM, and GIS for reliable simulation which predicts potential logistics risks and accurate module delivery time. Such reliable risk prediction enables effective supply chain coordination, which can improve project performance and the widespread application of modular construction.

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Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

Sensors ◽

10.3390/s21155031 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5031

Author(s):

Javier Villalba-Diez ◽

Miguel Gutierrez ◽

Mercedes Grijalvo Martín ◽

Tomas Sterkenburgh ◽

Juan Carlos Losada ◽

...

Keyword(s):

Real Time ◽

Sensor Network ◽

Industry 4.0 ◽

Numerical Control ◽

Manufacturing Processes ◽

Bayesian Decision ◽

Cnc Machine ◽

Digital Twin ◽

Decision Networks ◽

Computational Performance

With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.

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Urban Digital Twins – A FIWARE-based model

at - Automatisierungstechnik ◽

10.1515/auto-2021-0083 ◽

2021 ◽

Vol 69 (12) ◽

pp. 1106-1115

Author(s):

Martin Bauer ◽

Flavio Cirillo ◽

Jonathan Fürst ◽

Gürkan Solmaz ◽

Ernö Kovacs

Keyword(s):

Smart City ◽

Data Exchange ◽

Reference Model ◽

Smart Cities ◽

Digital Twin ◽

Data Exchange Platform ◽

City Community ◽

Digital Twins ◽

The City ◽

Different Time Scales

Abstract This article describes the use of digital twins for smart cities, i. e., the Urban Digital Twin (UDTw) concept. It shows how UDTws can be realized using the open source components from the FIWARE ecosystem that are already used in more than 200 cities worldwide. The used NGSI-LD standard is supported by the European Connecting Europe Facility, the Open and Agile Smart City community, the Indian Urban Data Exchange platform, and the Japanese Smart City Reference Model. Unlike digital twins in other domains, e. g., manufacturing, where digital twins are co-developed with their physical counterparts, UDTws often evolve driven by different stakeholders, on different time scales, as well as by utilizing many different data sources from the city. This article builds on a well-established lifecycle model for Digital Twins and combines this with a conceptual model for digital twins consisting of data, reactive, predictive and forecasting (“what if”) digital twin functionalities. The article also describes how AI-based technologies can be used to extract knowledge to build the UDTws from the IoT-based infrastructure of a smart city.

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