# Development and Application of Digital Twin Technique in Steel Structures
## 3 Literature Innovation Discussion
### Application Field  
Based on the definition evolution and the methodology development of the digital twin technique in the recent fifteen years, this technique has been applied in numerous fields of steel structures, referring to the collected literature. Specifically, these application fields in steel structure primarily involve four parts, which are material deformation, fatigue assessment, infrastructure management, real-time monitoring, and mechanical deformation property. The quantity proportion of the collected literature for these application fields is shown in Figure 10, from which the application field of the digital twin technique can be considered as broad-ranging. There is much research focusing on material deformation characteristics using the digital twin technique, such as elastic deformation, plastic displacement, fatigue failure, and alloy creepage. As shown in the diagram presented in Figure 10 from reference [69], the deformation and lifecycle analysis of steel materials were analyzed by an experimental study and a finite element analysis, based on which the predicted strain data from FEA simulations could map crucial strain and material deformation to test specimens. These characteristics can be reflected and simulated to varying degrees within the digital twin model; meanwhile, the indicators of material deformation in the model encompass the deformation mechanism [57; 138], deformation observation [50; 83], high-performance material deformation [20; 121], and complex deformation conditions [55; 91]. Through establishing the high-precision digital twin model and integrating an advanced simulation technique, the deformation mechanisms of materials are thoroughly studied, the complex deformation parameters are optimized, and the preventive measures are formulated. Infrastructure management is a prominent application area for the digital twin technique, particularly in critical structures, such as bridges, buildings, turbines, silos, and tanks. A digital twin model with an early warning mechanism of structural condition is usually  
Figure 10: Quantity proportion of the literature on digital twin application [24; 36; 69; 107]. the first step to establish by considering external dynamic loads. After that, the efficiency and safety of structural management are enhanced significantly by providing detailed maintenance guidance based on these models. Such as the diagram presented in Figure 10 from reference [36], bridge infrastructure was monitored by laser scanning to acquire raw data. After that, the digital twin model could be achieved based on point clouds and quality inspection. At last, infrastructure management could be conducted based on the established digital twin model. Many studies have also reported the application of this type in the maintenance of large-scale buildings [5; 65; 82], super-long bridge management [36; 95], advanced steel manufacturing [14; 99], and mechanical property evaluation [39; 122]. These application cases clearly demonstrate that the advantages and potential of infrastructure management and life cycle forecasting are based on the digital twin technique, which can optimize resource allocation and management decisions by simulating various operations and maintenance schemes. Fatigue assessment utilizing the digital twin technique has been applied in bridges, aircrafts, and other types of steel structures. As shown in the diagram presented in Figure 10 from reference [24], the fatigue information was firstly captured by a depth camera, and then the crack node in the image coordinate was determined. After that, the crack surface topology was reconstructed, and, ultimately, the three-dimensional topology of the crack was generated based on the digital twin technique. Subsequently, fatigue assessment could be conducted based on crack topology. Many studies have also reported its applications in various subjects, such as fatigue crack detection [1; 34], crack growth monitoring [25; 76], fatigue crack prediction [7; 92], and fatigue resistance evolution [68; 81].