# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 6 Problems and Prospects of Inversion Analysis Method  
The inverse analysis method has been used to solve relevant parameter inversion problems since the 1970s. It has been half a century since its inception, and during this period, many scholars have applied this method to various engineering fields. They continue to innovate and improve. With the rapid development of computer technology and observation methods, numerical analysis methods such as the finite element method and boundary element method, as well as monitoring methods such as ground-penetrating radar and advanced geological prediction TSP systems, have made the research of back analysis in tunnel engineering problems flourish. Despite this progress, there are still some problems in the current research of tunnel and underground engineering. The main findings are as follows:  
1. The reliability of initial geostress inversion in complex geological environments is low. Currently, the stress function used in the back analysis method of initial ground stress is relatively simple. Additionally, the existing theory of rock mechanics based on static research cannot fully reflect the stress field under complex geological conditions, such as high ground stress, high temperature, and long buried depth;
2. Lack of objective indicators to evaluate the accuracy of back analysis. The accuracy of the back analysis results mainly depends on whether the network model can establish an effective nonlinear mapping relationship. However, the types of network models are various, the level of obtaining data is uneven, and the actual engineering types are also different. Therefore, determining an index to objectively evaluate the accuracy of all kinds of back analysis methods has not been solved yet;
3. The process of data processing is difficult to share in real-time. Currently, a large-scale information platform for tunnel monitoring data and geological hazard information sharing has not been established. Furthermore, data mining has a high cost. As a result, some small and medium-sized tunnel projects cannot realize the dynamic optimization of the construction process according to the existing monitoring data. This increases the difficulty of this kind of tunnel construction. However, the quality of on-site data acquisition is also limited by excavation conditions and monitoring equipment. Coupled with the spatio-temporal variability of geological conditions, it is difficult to achieve a relationship between the network model and numerical analysis results in data analysis;
4. Intelligent back analysis methods are costly. The current artificial intelligence technology is not mature, and its way of dealing with problems involves rigorous analysis, calculation, and logical reasoning. This cannot be solved completely without human \begin{table}
\begin{tabular}{c c c} \hline
**Inversion Analysis Method** & **Project Cases** & **Brief Description of Analysis Results** \\ \hline \multirow{7}{*}{Neural network algorithm based on genetic algorithm optimization [95; 96; 97; 98]} & The large underground power station caverns engineering & The error between the calculated displacement value and the actual monitoring value is only within the range of 0.06–0.62 mm. \\ \cline{2-3}  & & The average absolute percentage errors of the optimized neural network model for predicting the tunneling speed of the composite stratum dual-mode shield machine are 2.312\% 2.926\%, respectively, while the standard neural network model is 4.343\% 7.240\%, respectively. \\ \cline{2-3}  & & Wuyang Tunnel of Xiarong Expressway Project & The predicted displacement is in good agreement with the measured displacement curve \\ \cline{2-3}  & & The errors between the numerical values of the vault settlement and clearance convergence of the studied section and the actual monitoring values are 13.2\%,\(-\)8.3\%,\(-\)8.9\% and 9.4\%, respectively. \\ \hline \end{tabular}
\end{table} 
Table 6: _Cont._intervention.