The Use of Digital Twin in the Building Industry 2 of 2

Data transmission layer

Raw data is transmitted i.e., transported, and processed from the previous layer mentioned above through general wire and wireless transmission technologies. The range is as follows: short-range wireless technologies, Wi-Fi wireless short-range technology, Bluetooth, wireless local area network (WLAN), ultra-wide-band (UWB) radio communication, a hybrid of both wired and wireless networks, Ethernet wired transmissions, internet and BACnet (Building Automation and Control Networks) protocols. (Tuhaise et al. 2023)

Conformance with communication layer protocols is a must for data transmissions. The so-called IEEE (Institute of Electrical and Electronics Engineers), and IETF (Internet Engineering Task Force) are the different protocol grouping and are further categorised into file transfer protocols and messaging protocols.  The transmission protocols are MQTT (Message Queuing Telemetry Transport), and HTTP (Hypertext Transfer Protocol). (Tuhaise et al. 2023)

In the Omrany et al. 2023  study DT data transmission is via technologies such as 1) wired options (i.e. coaxial cable, twisted pair, and optical fibre), 2) wireless options [ i.e. Zig-Bee, Wi-Fi, Bluetooth, ultra-wideband (UWB), and near-field communication (NFC)], 3) and long-distance wireless (GPRS/CDMA, digital ratio, wireless bridge, spread spectrum microwave communication, and satellite communication) and 4) also the emerging 5G and 6G. (Omrany et al.(2023)

Digital modelling layer

The physical entity becomes a virtual digital entity via development in the digital modelling layer. The modelling technologies measure the physical entity’s parameters. In the findings by Tuhaise et al. (2023), the technologies used are laser scanning to obtain the 3D point cloud model, Mixed Reality (MR), laser tape measurement, photogrammetry, and Modelling parametric design software. Also, the final product is represented by 3D model (BIM model). 3D modelling software were Autodesk Revit, Solidworks, Autodesk Navisworks, 3D Max, Rhinoceros 6 software,  Sketchup 3D, Autodesk Civil 3D,  AECOsim building designer, Unity game engine and Autodesk 3D Max,  Oculus touch controllers, Oculus Rift S VR headset,

Unity 3D platform, Unified Robotics Description Format (UDRF), Robot Operating Software (ROS), Three.js program and Midas Gen software.

The 2023 review by Elyasi et al. found that many technologies support FM. They are namely, BIM, Virtual Reality (VR), Computerized Maintenance Management System (CMMS), Augmented Reality (AR), IoT and DT. (Elyasi et al. 2023)

Data/model integration layer

Multi-source and high volume are features of high-fidelity DT requiring secure storage DT. Quick response codes, bar codes, and radio frequency identification (RFID) can be utilised for the safe storage in DT. However, big data storage technologies are required. According to Omrany et al. (2023) the big storage frameworks are HBase, MySQL, and NoSQL databases. In the findings by Tuhaise et al. (2023), the databases utiliised are cloud databases as follows: Google cloud platform, Internet my openHAB cloud, BIM cloud database, web database, cloud servers, Azure Microsoft for cloud storage, Alibaba cloud server, Cloud database and SQL server, Microsoft Azure SQL cloud database, Amazon Web service (AWS) DynamoDB, Heidi SQL, MySQL, MSSQL, ArangoDB database,  and influx database. Other technologies utilised for existing Building Management Systems are DynamoDB NoSQL schema, PHP interpreter server, Mongo database, and Apache web service.

Data fusion techniques are required “to provide human-understandable inferences” via “ customised APIs … built into the 3D model software platforms’’(Tuhaise et al. 2023). Also, API add-in plug-ins were developed for Autodesk Revit, Bexel Manager, Midas Gen software, Dynamo into Revit, Autodesk Forge, Autodesk Navisworks, and Three.js program. Other technologies used are Unity game engine platform by Unity, Data-smith tool, Oculus Rift S headsets, Unreal Engine 4 game engine, ‘Processing’ development environment, SophyAI online platform, Scheduled ETL (Extract, Transform and Load), Apache Kafka Flink, Brick schema, semantic data description, and IFC schema. (Tuhaise et al. 2023)

Processing and analysis are done via advanced technologies. In the findings by Tuhaise et al. (2023),   the techniques utilised are simple data analysis and AI which requires programming

and machine learning. The machine learning techniques technologies included the Markov model preparation and ANN (artificial neural network) training, Cumulative sum charts and machine learning, analysis of variance (ANOVA) and support vector machine (SVM), Apriori algorithms and complex network analysis, Markov chain, TensorFlow, Python  and Chronograf tool, Keras, Pytorch, Bayesian online change point detection, Bexel manager, Robot Operating Software, computation mechanical analysis, and 3D simulations. (Tuhaise et al. 2023)

A powerful aspect of DTs is the visualisation of temporal sensor data. In the findings by Tuhaise et al. (2023), the data visualisation was via 3D modelling platforms including Midas Gen software, Autodesk  Revit, Autodesk Civil 3D, Autodesk Navisworks, Autodesk forge, Unity 3D game engine, game engine platforms, Oculus Rift S VR headset,  Unity 3D, ROS (Robot Operating Software), Unreal Engine 4 game engine, AR (Augmented Reality), SophyAI online platform, Three.js program, and Node-RED dashboard.

Service layers

There is potentially a wide range of services via DT. In the findings by Tuhaise et al. (2023), the “most common service offered in the studies was real-time monitoring of assets and activities”. Beneficial functionalities of DT were early detection of potential failure, warning of potential accidents, alarm signals, visualization of environmental, and indoor ambient conditions,  thermal comfort levels, construction progress, space use, fan coil status, simulations, and finally robotic operations and home appliances controlled in real-time. (Tuhaise et al. 2023)

Benefits of DT

Various reviewers identified the benefits of DT, and they are listed in Table 2 on page  21 entitled Benefits of DT and includes the source. Some of the listed benefits are generalised and doubling-up but others are more defined.

With suitable appropriate technology, DT facilitates data gathering in real-time, data monitoring, and data-based decision-making and supports forecasting. As there is enabled continuous information and data flow, the DT continuously collects and adapts to operational changes and provides predictions. This means maintenance strategies can be forecast. (Elyasi et al. 2023, Esmaeili et al. 2023).

Challenges and opportunities of DT

With their extensive studies, Tuhaise et al. (2023) concluded, that although DT is still in its nascent phase in the building industry, it has the potential to improve the building industry’s productivity and performance because DT provides “real-time condition monitoring, and predictions, simulations, and optimization processes which enable effective decision making”. The dynamic environments for developing DT and IoT are opportunities presented in university campuses and schools. (Opoku et al. 2023)

The construction industry is complex by nature. Buildings are designed differently, some are massive, and modern buildings are equipped with smart automated systems and heterogeneous systems. The ascent of the building of intelligent and smart buildings provides a good reason for the implementation of  DT. Through the findings, Tuhaise et al. (2023) decided that “some challenges and opportunities were identified in three areas namely 1) data transmission 2) interoperability and data integration, and 3) data processing and visualisation”. In the proposal, Yoon (2023) identified challenges and research directions as being 1) “In-situ modelling and model fusion methodology, 2)  Synchronization between physical and digital building operations, and 3) Measurements and verification (M&V) for digital elements”. (Madubuike et al. 2022)

In their reviews, Opoku et al. (2023), Madubuike et al. (2022), Elyasi et al. (2023), Omrany et al. (2023) and Esmaeili et al. (2023) clearly identified the barriers to the use of DT in the building industry and they are listed in Table 4 on page 24 entitled Barriers to the use of DT and includes the sources. Interestingly, Opoku et al. 2023 identified thirty barriers to the use of DT in the building industry. They are similar to each other, but Opoku et al. (2023) worded them differently, and more importantly, ranked them.

Undoubtedly, in accordance with Madubuike et al. (2022) words, a DT is a significant upfront investment for the stakeholders. The thirty Opoku et al. (2023) barriers are organised further into four categories namely, i.e., “1) stakeholder-oriented barriers, 2)industry-related barriers, 3)construction-enterprise-related barriers, and 4) technology-related barriers” (Opoku et al. 2023).

The literature review by Elyasi et al. (2023) involved interviews with experts in the AEC and an examination of the literature. In the review,  Elyasi et al. (2023) highlight there is resistance to change and a low level of innovation in the AEC that does not facilitate the adoption of DT. With their focus being DT use in the facilities sector, the most common standard for BIM is the Industry Foundation Class (IFC) format. In fact, the IFC has not been directly developed to implement DT. In the facilities management sector (FM),  DT is powered by IoT sensors, with the model connected with maintenance-related documents and it interacts with FM software and a CMMS system. As DT comprises data sources, many people, and components it is relatively easy to implement but complicated to maintain as it requires more resources and competencies. Several people confirm robot infrastructure is needed for DT in FM. With DT,

there is a monitoring stage including a digital model and a digital shadow, then an intelligent semantic stage of DT, and lastly an agent-driven socio-technology stage of DT. So far as the theoretical background is concerned, new definitions and classifications are emerging. ( Elyasi et al. 2023)

The range of emerging technologies that are being used in DT is wide. For the sake of worldwide expansion, can data be transmitted by long-distance wireless technologies such as satellite communication and digital radio instead of short-range wireless? According to Tuhaise et al. (2023), the acquisition of DT “data from heterogeneous systems, networks, and devices increases the complexity of data transmission”. Are there other standard communication protocols that can be investigated and used other than the standard transmission protocols MQTT and HTTP? In the findings by Tuhaise et al. (2023), only three studies considered the security requirements of protecting confidential data, its transmission, and the DT product. Can ‘smart contract’ and ‘blockchain technology’ be integrated into DT to increase network security and comprised data attributability? Are context-aware privacy policies and privacy-preserving networks to be included in DT products? (Tuhaise et al. 2023)

Also, in their extensive studies, Tuhaise et al. (2023)  identified 1) “ there is a need to investigate semantic data modelling of sensor data, BIM model data and data from other systems to aid in moving towards standardising DT data by enabling data integration and interoperability, 2)  the use of web-based semantic ontologies for digital twin data integration should be explored, 3) various methods of visualising ……abstract parameters within a digital model should be explored (Tuhaise et al. 2023)”.

In the 2023 review, Yoon proposed a “ novel framework and methodology for building digital twinning (BDT) over the life cycle of a building” as per Figure 1 on page 27. The proposal was to “ establish an intrinsic” DT “framework and methodology in the building sector” (Yoon 2023). It emphasized, “ data, information, and models (DIM) in terms of physics, states, and behaviours for the target buildings, thus providing the structures to construct, extend, and manage DIM throughout the building operations” (Yoon 2023). The author believes the acronym DIM model relates to Data-Information-Meaning.  One challenge would be to produce the BDT proposed by Yoon. (Bing 2023)

The review by  Opoku et al. (2023) reports DT may be applied in the conservation of and  “safeguarding heritage assets that may have to be demolished soon”. In the Esmaeili et al (2023) study they proposed a structured framework for DT use in the building phase from the perspective of a contractor as shown in Figure 2 on page 28 naming it construction DT (CDT). They in turn applied CDT to a soil management study to establish its practicality. The Omrany et al. (2023) report provided a summarised list of the barriers that can be addressed.

Photo by author. The above is part of a university dissertation by the blog’s author with  references being as listed on The Use of Digital Twin in the Building Industry 1

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