# Literature Review of Digital Twins Applications in Construction Workforce Safety
## 5 Sensor Technology in Construction Safety
### Identification of Unsafe Construction Environment  
The occurrence of unsafe conditions is unavoidable in the construction site. The challenge is to identify and deal with the unsafe condition in a timely manner. Given the variety of conditions that may occur during dynamic construction activities, such as scaffolding damage, loss of guardrails, and movement of heavy equipment, ensuring that every unsafe condition is detected before the occurrence of an accident relies heavily on human efforts, which are limited on-site. Therefore, sensor technology can be used to automatically monitor the construction environment. [PERSON] et al. (2019) developed a system to estimate the status of scaffoldings, including safe, over-turning, uneven settlement, and over-loading, by fitting strain sensors onto the scaffoldings. They developed a model to distinguish different  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Branches** & **Sub-Branches** & **Number of Articles** \\ \hline \multirow{4}{*}{Sensor technology in construction safety} & Identification of unsafe construction environment On-site individual positioning Workforce behaviour monitoring & 14 \\  & & 16 \\ \hline Virtual construction simulation and visualisation technology & Safety planning Construction activity visualisation Safety training & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of articles in different research branches.  
Figure 5: Number of articles published in the selected journals.  
safety conditions of scaffolds through the combination of finite element analysis (FEA) and machine learning. The system has the potential to estimate the status of reinforced concrete structures by introducing material performance [44] and structural behaviour [45]. The dynamic model is able to accurately reflect the real situation [46]. Another monitoring method based on transfer learning and a convolutional neural network (CNN) was developed by [PERSON] et al. [47]. The method detects the installation of safety guardrails through the 2D image captured by a camera, which has the potential to ensure the effective implementation of a safety plan. Heavy equipment moving around the construction site, including excavators, also pose a hazard area to those in the surrounding area. [PERSON] et al. [48] proposed a spatial prediction method to assess traffic issues. [PERSON] et al. [49] combined the images captured by multiple cameras at the construction site to form stereo vision to estimate the 3D pose of the excavator. The researchers digitised the situation of a physical twin to a digital twin. Through the analysis of the digital twin, human-readable information is extracted from a vast amount of abstract data, which eliminates the complicated manual processing, and effectively and reflects the monitoring content of the construction environment in a timely way.  
With the help of workers' safety experience and behaviour, some methods have been proposed to identify unpredictable, dangerous situations and hazard areas to fill the gap between the safety plan and the real construction situation. [PERSON] et al. [50] developed a method for automatic identification of hazardous areas by monitoring workers' movements. They compared the optimal route with the route chosen by workers to determine the areas where they refuse to enter. With the help of the algorithm, these areas can be analysed to determine whether there is a hazard. Another method involves collecting workers' gait abnormalities by having them wear inertial measurement unit sensors (IMUs) around their ankles. The gait abnormalities were found to be strongly correlated with the presence of hazards, such as slippery floors and uneven floors [51]. [PERSON] et al. [52] introduced fuzzy inference to judge the safety level of everyone on a construction site at any given moment by extracting spatial information about on-site entities, including individuals and equipment.