# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 5 Inversion Analysis of Characteristic Parameter
### Intelligent Inversion Analysis Method  
The parameter inversion technique has a solid foundation, especially with the increase in the types of field measurement information. The application of uncertain parameter identification methods and intelligent rock mechanics methods makes the result of parameter inversion more unique and reliable. However, the current method based on the optimal solution to solve the parameter inversion analysis problem will invert all the parameters involved in the tunneling project and cause the phenomenon of dimensional disaster. Therefore, it is not realistic to take all the undetermined parameters as the design variables for the inversion analysis, and the relationship between the measurement information and the mechanical parameters is usually highly nonlinear. The singularity of the measurement information will also lead to the deterioration of the uniqueness of the identification parameters. With the promotion of the finite element analysis method, the use of finite element software combined with deep neural network models such as genetic algorithms [75], particle swarm algorithms [76], and simulated annealing algorithms [77] for parameter inversion analysis has developed rapidly. Deep learning algorithms can achieve an infinite approximation of various functions and establish nonlinear mapping relationships between data to obtain target parameters under complex constraints, effectively avoiding the problem of data dimension disaster. In the study of parametric inversion analysis using the finite element method combined with an intelligent optimization algorithm, [PERSON] and [PERSON] [78] used the combination of the extended Bayesian and finite element analysis to predict ground response and determineotechnical parameters. [PERSON] et al. [79] considered the loss of displacement in the monitoring process, combined the traditional Gaussian process with a particle swarm optimization algorithm, and proposed a new parameter back analysis method based on FLAC\({}^{3\text{D}}\) numerical analysis software, which can evaluate the stability of deep underground geotechnical engineering. In order to make up for the deficiency of traditional neural networks and support vector machines in three-dimensional displacement back analysis, [PERSON] et al. [80] proposed an improved combined kernel Gaussian regression algorithm. With the combination of particle swarm optimization and the algorithm, accurate mapping between geomechanical parameters and rock mass deformation is realized. The simulation example of the Beikou tunnel shows that the recognition accuracy of the algorithm meets the engineering requirements. [PERSON] et al. [81] introduced the elastic-plastic stress-seepage  
Figure 10: The dual boundary control model [73]. damage coupling model into the intelligent displacement back analysis and realized the intelligent inversion of tunnel surrounding rock damage parameters. The analysis process, as shown in Figure 11, is divided into three parts, corresponding to (a)-(c), in which part (a) describes the whole calculation process of the intelligent back analysis program. Part (b) briefly explains the implementation process and principal formula of the differential evolution algorithm used in the program, and part (c) shows the calculation results of the elastic-plastic damage mechanical field of surrounding rock and lining after tunnel excavation with and without considering the action of seepage. [PERSON] et al. [82] used three-dimensional numerical simulation software to calculate the training and test samples for the component extreme learning machine model. They combined tunnel field displacement and stress monitoring data and, through the optimization of differential evolution algorithm, realized the back analysis of surrounding rock parameters in highway tunnel engineering in Fusong City, Jilin Province. [PERSON] et al.