The Use of Digital Twin in the Building Industry 1

INTRODUCTION

A digital representation of a real object is a DT. The same is also called data twin, or virtual model. In the aerospace engineering field,  the  “ NASA Technology Roadmaps – NASA’s Apollo project in the 1960s” mentioned ‘twin’ in relation to a physical system (Madubuike et al. 2022).  The NASA Apollo 13 mission of 1970 utilised the first data twin. In the building industry application, a DT is an exact replica of a project which could be one or more buildings, a city block, a whole city, a motorway, and/or a bridge. (Madubuike et al. 2022, Trimble 2023)

Bloggers, you are not seeing double we have presented a digital twin!  Since 1970, we have been in the age of global capitalism with ever-developing technology systems such as microelectronics, robotics, biotechnology, information systems, and digital telecommunications.  It was Michael Grieves who originated the concept of the DT in 2003 and at ASME’s DT Summit, he discussed the future of DT. Mr. M. Grieves determined DT has generic components such as virtual entity, physical entity, and the data connection between both of those. (ASME 2023, Gonzalez 2023, Madubuike et al. 2022, Tuhaise et al. 2023, Wiatt et al. 2000)

Real-world information about a  structure is gathered and combined using dynamic technologies such as drones, cameras, sensors, 3D laser scanners, and other IoT-related devices. (Trimble 2023)

Airtel business explains the dynamic Internet of Things i.e., IoT “is a system of interconnected physical devices that communicate via network connectivity using various communications protocols”(Airtel  2023).

Via meticulous inspection of the real world, a DT provides information on physical orientation, shape, motion, and insight into leaking pipes and stress loads.  Stakeholders can access DTs through software “such as Trimble Connect, Quadri, Agile Assets, or Tekla” in a centralised source (Trimble 2022). (Madubuike et al. 2022)

In the last two decades, there have been technological advancements in the computerised world. We have Industry 4.0,  intelligent computers, DT, and cyberphysical systems (CPS) that have been harnessed by many industries other than construction. There are inherent problems in the construction industry. The sector is pollutive, low-efficient, and high waste. Adoption of digital solutions including DT technology is an ideal tool for creating management systems and could be the solution to solving the problems. A DT is not confused as being the same as BIM because, in fact, it addresses the limitations of BIM. (Tuhaise et al. 2023, Xie et al. 2023, Yoon 2023)

According to M. Grieves the future of DT will marry in with artificial intelligence (AI), and in fact calling it advanced AI-driven DT. Industry 4.0, intelligent computers, CPS, and DT do relate to the building industry. (Esmaeili et al 2023, Gonzalez 2021)

The literature research will seek the benefits, challenges, and opportunities of the use of DT in the building or construction industry. The research methodology aims to follow established avenues and approaches. The intention is to use the mixed secondary research method.

RESULTS

Entities and data connection in the building or construction industry

With a DT system, “the physical entity is connected to its equivalent virtual model by a data connection that allows data exchange” between the two entities. A DT provides “continuous information flow between a physical and virtual asset throughout the product lifecycle of the asset” (Madubuike et al. 2022). The review by  Opoku et al. (2023) reports DT “ utilises real-time data, whilst BIM works with static data”. In relation to DT technology, building information modelling (BIM) is the starting point to develop the connection “to the physical environment to enable the bi-directional transfer of data between both entities. This enables the BIM model to be updated with real-time data which facilitates improved decision-making in the implementation and management of assets. Moreover, DTs leverage advanced data analytics techniques like AI for processing large sets of data to enable condition monitoring, predictions, diagnostics, prognostics, and system optimisation. These DT capabilities have the potential to significantly improve information management and decision-making in various construction practices which in turn enhances the efficiency of construction and asset management activities” (Tuhaise et al. 2023). The review by  Opoku et al. (2023) reports, 1) the use of DT has been adapted towards the use of BIM models at the design and engineering stage of a project, 2) DTs have been focused on the structural integrity and cost reduction of the project’s system in the construction stage, 3) applications of the DT focus on the management of facilities, maintenance of facilities, processing of logistics, monitoring, and energy simulations of projects at both the operation and maintenance (O&M) stages. (Opoku et al. 2023, Tuhaise et al. 2023)

The application of DT in the building industry is an area of research that is growing. In the building industry, a definition of DT can be addressing a project or a particular purpose of the industry. In any case, according to Yoon (2023), DT use has untapped enormous potential in so-called grid-interactive building operations, building-level optimal operations, indoor air, and environmental qualities, the energy efficiency of a building, and a building’s carbon neutrality. Reportedly, there are significant challenges in the technological development of DT due to a variety of emerging technologies. (Opoku et al. 2023, Tuhaise et al. 2023, Yoon 2023)

A high-fidelity, virtual model of the physical entity is generated via modelling technologies mirroring physical environment parameters such as state, functionality, location, performance, process, and geometric structure. The IoT technologies are used to enable a connection that allows the data’s bi-directional transfer between the virtual and physical entities within established digital layer protocols which are number five.  (Omrany et al. 2023, Tuhaise et al. 2023)

Buildings are embedded with actuators, automated sensors, and other technologies that serve as the bridging data connection to monitor and control physical assets. Table 1 below provides an insight into components of DT and current technologies in use in the building industry as published by Tuhaise et al. (2023) showing the varied application of DTs to diverse entities in the physical environment in relation to buildings, civil engineering structures, building components, machinery, workers, site resources, and even at the city level. (Madubuike et al. 2022).

The review by Madubuike et al. (2022) disclosed a DT has been implemented in Singapore’s Frasers Tower with Bentley Systems and Schneider Electric collecting “data using a mix of 179 Bluetooth beacons in meeting rooms and 900 sensors for lighting, air quality, and temperature”. Their “platform uses embedded sensors and telemetry to generate about 2,100 data points connected to the cloud using Microsoft Azure to create enabling holistic management of the environment” (Madubuike et al.2022). The same study found a DT purpose application for 1) monitoring of construction progress, 2)monitoring of the state of a bridge via a geometric DT of it developed through an automatic process of generating 3D models from point clouds which were captured by laser scanners. Also, Agapaki et al. (2018)  identified that the 5,200 man-hours were spent in modelling 53,834 pipes. (Madubuike et al.2022)

The study by Elyasi et al. 2023 found some examples of the examination of DT use in the facilities management (FM) sector of the construction industry namely 1) enhance the O&M phase of tunnels, 2) maintenance of wind turbines, 3) a framework for DTs, 4) detection of anomalies in connection, 5) transforming 2D pictures into 3D model via a data-driven method. Regardless, the actual application of DT in practice is advancing slowly. (Elyasi et al. 2023)

The study by Tuhaise et al. (2023) provides a succinct list of DT implementation in the building industry with the most used technology being IoT. Other technologies that were used include image sensors, laser scanning,  radio frequency identification, satellite positioning and recognition devices, image recognition artificial intelligence, vision, and component-based sensing systems, image recognition AI, and Restful Application Programming Interface. The virtual entities are a mix of BIM and  3D models. (Tuhaise et al. 2023)

The review by Omrany et al. 2023 disclosed the Ezhou Huahu International Airport project, in Hubei Province (situated in the eastern region of Ezhou),  To develop a DT, China utilised so-called Bentley BIM and iTwin technology. The DT enabled continuous integration of data components and extensive engineering. Due to a 200-day reduction in the project’s delivery time and considerable monetary savings, the implementation of the DT was highly effective. They identified the directions of DT in the building industry namely, 1) virtual design, 2) Project Planning and Management (PPM), 3) Asset Management and Maintenance (AMM), 4) Safety Management, 5) Energy Efficiency and Sustainability (EES), 6) Quality Control and Management (QCM), 7) Supply Chain Management and Logistics (SCML, and 8) Structural Health Monitoring (SHM). Their remarkable findings are summarised in Table 2 below.  (Omrany et al. 2023)

DT Classification

The study by Elyasi et al. (2023) stated a model was presented by Errandonea et al (2020) being the first classification of DT as a distinguishment between a digital model, digital shadow, and DT. Additionally, a second classification of DT was based on the DT Maturity Index that Meda et al. (2021) and Fjeld (2020) presented where a BIM is enhanced with Iot. (Elyasi et al. 2023)

Five layers of technologies

The five layers of technologies in DT applications are 1) data acquisition, 2) data transmission, 3) digital modelling, 4) data/model integration, and 5) service layers. Owing to their studies, according to Tuhaise et al. (2023) further classifications of data/model integration are “ data storage, data/model integration and fusion, data processing and analysis and data visualisation” providing an extensive insight into the layers and current technologies in use in the building industry. (Tuhaise et al. 2023).

Data acquisition layer

In the findings by Tuhaise et al. (2023) dynamic acquisition of data from the physical environment was via IoT sensors and technologies. The wide range of sensors are as follows: DHT11 sensors, Monnit wireless sensors, Rev. P wind sensors, DHT22 sensors, TA465-X sensor,  Sensortag CC2650 light sensors, Texas Instruments, PT550 light sensors, Restful API over a conventional BMS to collect data of the BMS hard-wired sensors that include NTC-12 K-sensors, TTH-6040–0 sensors, IVL10 temperature-sensitive airflow transmitters, PTH-3202-DR sensors, RESTful API, sensor data from the  Supervisory Control and Data electric acceleration sensor, steel column tension-compression sensor, accelerometer with a displacement sensor, current sensor, vibration sensors, EU:77DE-06-09 voltage sensor DS18B20 and PT100 temperature sensors, soil moisture sensors, asphalt strain sensors, inductive displacement network sensor, network sensors, horizontal inclinometers, surface sensor pressure pads to obtain real-time data, switch sensors, load cells, accelerometer and displacement sensor, ultrasonic sensor, gyroscopic sensors, laser ranging sensors, vision and wearable IMUs (Inertia Measurement Units), Velcro vive trackers, camers and laser scanners, building surveillance system, FLIR lepton 2.5 thermal imaging module connected to a Raspberry Pi microprocessor, Smart cameras provided video streams, high-quality Bullet Pro Camera sensors, Microsoft Kinect cameras, cameras with image sensors coupled with image recognition (Im) artificial intelligence,  3D point clouds obtained using a UAV (Unmanned Aerial Vehicle) equipped with a LiDAR scanner, RFID tags, RFID systems,  UWB tags and the global navigation satellite system (GNSS), Aurdino Mega 2560 R3 microcontroller board, sensor platforms such as  NodeMCU microcontroller, Raspberry Pi 3B+ and  Raspberry Pi 3. (Tuhaise et al. 2023)

Technologies such as image recognition measurement, laser measurement, conversion measurement, micro/nano-level precision measurement, and IoT are credited by Omrany et al.(2023).

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