# Development and Application of Digital Twin Technique in Steel Structures
## 3 Literature Innovation Discussion
### Methodology Development
The machine learning method can be considered an advanced surrogate model to predict future structural behavior when it is used in the digital twin technique, primarily performed in the program \"Python 3.10\". The machine learning model has potential in reflecting the condition and performance of physical structure by real-time data updating, and, therefore, robust support for decision-making can be provided, such as the topological structure of a neural network, provided by reference [78] in Figure 9. In the above literature, some research focuses on algorithm innovation in machine learning [8, 105], some on the data-driven effects of the fully trained model [11, 88], and others on the combination of the digital twin concept and machine learning [64, 102]. This research mainly indicates that the machine learning method obtains superiority in dynamic prediction, structural optimization, and cross-field applications. The literature distribution for the aforementioned methodologies of the digital twin technique is illustrated in Table 2.  
It is clearly observed in Figure 9 and Table 2 that the finite element method has occupied the largest proportion in these digital twin methodologies, while the BIM model method presents the second proportion, and the machine learning method has the least proportion. The reason lies in that the digital twin models of steel structures, established by the finite element, BIM model, or laser scanning, all developed fully in recent years, while the machine learning method has just begun to be adopted as a surrogate model of steel structures. Compared with these digital twin methods, the finite element method has an advantage in characterizing structural, mechanical, and parametric analyses and model synchronization and updating. The BIM and laser scanning methods focus on structural visualization and data management. The machine learning method can make full use of structural monitoring data and obtain crucial structural properties, having more potential in future development. Mentioned that the combination of these methods can \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Subject** & **References Number** \\ \hline \multirow{3}{*}{Finite element} & Algorithm fusion & [3,7,9,24,31,43,48,70,81,89,109] \\  & Monitoring technique & [4,16,39,49,59,77,93,104,107,117] \\  & Application effect & [18,22,25,32,40,41,55,69,80,92,119] \\ \hline \multirow{3}{*}{BIM model} & Date exchange & [6,14,28,34,115] \\  & Framework establishment & [5,27,53,62,66,99,101] \\  & Management effectiveness & [29,63,72,74,103] \\ \hline \multirow{3}{*}{Laser scanning} & Point-cloud analysis & [37,45,84,90] \\  & Model establishment & [12,17,19,33,36,67] \\ \cline{1-1}  & Model function & [26,50,95,122] \\ \hline \multirow{3}{*}{Machine learning} & Algorithm innovation & [1,8,105] \\  & Data-driven effect & [11,23,54,88] \\ \cline{1-1}  & Model fusion & [30,52,60,64,78,102] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Literature distribution of digital twin methodology. fuse their results and perform their advantages in the digital twin process, which is from data monitoring, model updating, and structural condition maintaining.