# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 5 Inversion Analysis of Characteristic Parameter
### Intelligent Inversion Analysis Method
[83] put forward a new displacement back analysis method by combining neural networks, evolutionary algorithms, and numerical analysis methods, and applied it to the establishment of rock mass mechanical parameters of the Three Gorges Project. [PERSON] et al. [84] aimed at the problems of overfitting, local optimization, and poor generalization ability of neural networks. They used a direct optimization algorithm based on a genetic algorithm and improved support vector machine to analyze the rock mechanical parameters and initial  
Figure 11: Intelligent inverse analysis of coupling parameters: (**a**) calculation process of intelligent back analysis program, (**b**) difference evolution algorithm, (**c**) calculated mechanical field of elastic-plastic damage in the surrounding rock and lining after tunnel excavation with and without considering the effect of seepage [81]. stress of an eccentric tunnel in the shallow section of Zhangshi highway. [PERSON] [85] coupled a genetic algorithm with combined covariance-Gaussian process and, also taking Beikou Tunnel as an example, put forward an optimization method for support parameters, and formed a complete information construction method for tunnel engineering, which was carried out with the prediction results of the genetic algorithm and support vector machine regression coupling algorithm. The results show that this method has the advantages of simple operation, fast processing speed, and high precision. In general, problems can be solved using artificial neural networks, genetic algorithms, simulated annealing, ant colony algorithms, and hybrid optimization algorithms that improve upon the aforementioned algorithms. However, when there is limited observational data and numerous inversion parameters, the probability of distortion and ill-posed phenomena in the inversion results increases significantly. To address this issue. [PERSON] [86] started with the finite element equilibrium equation and used the local model solution approximation as a global model solution for one or more materials. This approach can reduce errors and solve distortion in the inversion results, greatly expanding the applicability of the original algorithm. Tables 5 and 6 display the application cases of the traditional inversion analysis method and classical intelligent inversion analysis algorithm in engineering. It can be seen that the improvement measures of various optimization algorithms, such as improved particle swarm optimization algorithm [87], genetic simulated annealing differential evolution algorithm [88], and immune algorithm [89], can further enhance the optimization ability and inversion quality by integrating the advantages of each algorithm and eliminating their disadvantages. These algorithms are widely used in the calculation of various tunnel characteristic parameters. \begin{table}
\begin{tabular}{c c c} \hline
**Inversion Analysis Method** & **Project Cases** & **Brief Description of Analysis Results** \\ \hline Support vector regression based on multi-strategy artificial fish swarm algorithm [92] & Heshi Tunnel in Xiushui County, Jiujiang City, Jiangxi Province & The training and testing accuracy of the mechanical parameters of the tunnel surrounding rock is significantly improved, with the relative error only within 0.1\% \\ \hline Deep learning network architecture based on GPRInvNet [93] & Data set of tunnel lining defect types & The location of tunnel lining defects is effectively inverted, but due to the complexity of GPR data, only the contour of defects can be reconstructed \\ \hline Difference evolution algorithm [94] & Shield Tunnel of Guangzhou Metro Line 9 & The maximum error of the inversion results is 20.8\% and the minimum is 0.72\% \\ \hline \end{tabular}
\end{table} 
Table 6: The statistical results of engineering cases for characteristic parameter in intelligent inversion analysis.