# Development and Application of Digital Twin Technique in Steel Structures
## 4 Conclusions and Recommendations  
This review about the development and application of the digital twin technique in steel structures, based on the latest literature from the past fifteen years, was conducted to analyze its development in recent years and to discuss its application in the field as follows:  
1. The bibliometric analysis of the literature focused on the publication details, keyword information, and application specifics, indicating that the digital twin technique in steel structures had developed rapidly, with most publications hailing from the Asia-Pacific and European regions. The keywords emphasized that the function of the digital twin technique can be the monitoring, maintenance, and assessment of steel structures. Meanwhile, usage exploration around bridges, buildings, and materials indicated that the digital twin technique was first applied in the research on material aspects and then used in bridges and buildings. 2. The discussion of the literature innovations primarily addressed the evolution of digital twin definitions, the development of modeling methodologies, and the corresponding application fields. These definitions evolve from a digital image to a virtual model and then to a surrogate model, and these models can gradually deal with more complex features of a structure. Meanwhile, these methodologies demonstrate their respective advantages in monitoring data usage, model visualization, and updating. Moreover, the digital twin technique is suitable to apply in various fields of steel structure, especially in healthy monitoring and condition maintenance, while different application fields may be appropriate for different digital twin methodologies. Based on the above conclusion, it is obvious that future research orientations for the digital twin technique in steel structures mainly lie in the innovation of the digital twin model, the expansion of application situations, and the optimization of model performance as follows:  
1. Model innovation should focus on the multiple-dimension model and high-precision modeling, where the established model not only has the ability of data utilization with \begin{table}
\begin{tabular}{l l l} \hline
**Application Field** & **Subject** & **References Number** \\ \hline \multirow{4}{*}{\begin{tabular}{l} Material \\ deformation \\ \end{tabular} } & High-performance material deformation & [20,35,37,87,94,106,108,109,112,118,121] \\  & Deformation observation & [50,69,83] \\  & Complex deformation condition & [47,55,69,91] \\  & Deformation mechanism & [57,85,97,100,113,124,125,127,128,136,137,138] \\ \hline \multirow{4}{*}{\begin{tabular}{l} Infrastructure \\ management \\ \end{tabular} } & Large-scale facility maintenance & [5,19,33,65,70,82] \\  & Bridge management & [36,40,41,80,95] \\  & Advanced steel manufacturing & [14,21,26,59,77,99] \\  & Steel mechanic assessment & [39,54,78,84,89,102,122] \\ \hline \multirow{4}{*}{\begin{tabular}{l} Fatigue \\ assessment \\ \end{tabular} } & Fatigue crack detection & [1,8,10,22,34] \\  & Crack growth monitoring & [25,76] \\  & Fatigue crack prediction & [7,16,24,73,92] \\  & Fatigue resistance evolution & [68,81] \\ \hline \multirow{4}{*}{\
\begin{tabular}{l} Real-time \\ monitoring \\ \end{tabular} } & Structural condition identification & [4,6,45,52,62,74,107] \\  & Steelmaking monitoring & [12,13,51,63,88,115,126] \\  & Large-scale facility manufacturing & [17,39,71,104] \\  & Structural measurement & [31,88,96,103] \\ \hline \multirow{4}{*}{
\begin{tabular}{l} Other \\ applications \\ \end{tabular} } & Special material property & [11,15,23,38,42,44,49,60,64,75,98,101,111,117] \\  & Advanced measurement & [18,27,28,30,58,79] \\ \cline{1-1}  & Typical manufacturing process & [29,46,72,109] \\ \cline{1-1}  & Special industry & [53,66,119] \\ \hline \end{tabular}
\end{table} 
Table 3: Literature distribution of digital twin application. large amounts and heterogeneous properties but also can reflect the multi-property of a steel structure, such as the surrogate model combined with machine learning. 2.