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.

Automated summary

Digital twin (DT) is a key technology for implementing Industry 4.0 in construction and smart cities, connecting different objects to improve productivity and reduce energy consumption. Its characteristics such as bi-directional data exchange and real-time self-management distinguish it from other information modeling systems and make it a core technology in post-COVID-19 industrial sectors.