# Literature Review of Digital Twins Applications in Construction Workforce Safety
## 1 Introduction  
The construction industry has long been plagued by safety issues. According to statistics, there has been no significant reduction in the number of occupational fatalities in major countries or regions over the past five years [1]. According to the China state administration of work safety, 3843 fatalities occurred in China in 2017 [2]. In the U.S., the death toll was 1038 in the construction industry in 2018, accounting for one-fifth of all industry fatalities [3]. The uniqueness and complexity of construction projects make it difficult for the sector to avoid occupational injuries and fatalities [4]. For example, site engineers may lack situational awareness and fail to take measures to identify and prevent imminent hazards. Digital twins (DT) is a concept of creating a digital replica (the digital twin) of a physical object (the physical twin) and synchronising data from physical twin to digital twin to achieve monitoring, simulating, and optimising the physical object [5]. DT concept was originally applied in the aerospace field [6] and proliferated to other industries with the development of technology. In manufacturing industry in particular, it is not only used to monitor the condition of products, but also to monitor production lines [7, 8]. In recent year, the smart building and the smart city gradually adopted the concept of DT to achieve the digital management of the construction products [9, 10]. At the same time, DT also provides an innovative approach to construction management [11] (Figure 1). Sensors are a significant underpinning technology component when formulating a typical DT application. There are many types of sensor, including global positioning system (GPS) sensors, radio frequency identification (RFID) sensors, wireless local area network (WLAN) sensors, Bluetooth low-energy (BLE) sensors, ultra-wideband (UWB) sensors, and others [12; 13; 14; 15; 16; 17]. Machine learning technology has been well integrated into computer vision to enable object recognition, tracking, and pattern analysis [18; 19]. Advanced visualisation technology is helping to improve construction safety as well [20]. The visualisation technology presents dynamic and complex information generated by DT, helping construction managers and on-site workers to make decisions. Building information modelling (BIM) [21], virtual reality (VR) [22; 23; 24; 25; 26; 27], and mixed reality (MR) [28; 29; 30] are good examples of this. The identification of hazard factors helps guarantee the safe management of all stages of construction. By obtaining relevant data information through BIM, VR, and AR techniques, workers can carry out advance simulations of construction safety management and effectively improve the orderliness and safety of construction activities. Workers can see the danger they are facing and determine the correct mitigation plans to avoid accidents to the greatest extent possible. Although numerous studies have examined the effectiveness of the DT methodology, a comprehensive, systematic, and evidence-based review is lacking of all individual articles that investigated the effectiveness of using DT in the workforce safety space. For instance, [PERSON] et al. [4] proved that DT can effectively prevent injuries caused by unsafe location-based behaviours, but this would require the support of sensing and tracking technologies such as danger zone identification [16], individual positioning [13], posture estimation [19], semantic analysis [11], and real-time alerts [20]. An examination of this body of evidence with regard to these methodological problems has not been conducted either. Therefore, the objective of this study was to disclose the progress of the current research and determine the relevant gaps, challenges, and future work. CiteSpace was employed to summarise the research trends, advancements, and frontiers of DT applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations, and future research directions.