# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 2 The Development and Main Research Contents of Inversion Theory
### Development of Inversion Theory
#### 2.1.4 Intelligent Phase  
Continuous innovation in computer technology and field measurement technology since the early 21 st century has addressed problems of missing data, low accuracy, and weak dynamics in numerical analysis. As a result, the inversion analysis method has entered the intelligent stage. Researchers have started using a method that combines artificial intelligence technology with the basic theory of tunnel and underground space engineering to carry out intelligent analysis of the mechanical behavior of the rock mass during excavation. This approach enables rapid numerical simulation analysis and research according to different construction methods.  
The main feature of the intelligent phase of inversion analysis is the introduction of deep neural networks, which have powerful ability to solve nonlinear problems with low workload, short time consumption, and high accuracy. Large numbers of parameter values are not required in engineering problems, such as parameter identification and deformation prediction. Instead, relevant information about the research object can be obtained through network analysis. A cloud database is established, and after uploading the data, end-to-end intelligent inversion analysis can be performed [13; 14; 15; 16; 17].  
Representative achievements in this stage include [PERSON] et al. [18], who used on-site monitoring information of the concrete dam of Shuibuya Hydropower Station to invert the dam rockfill parameters. The inverted parameters were used to re-calculate the dam displacement using a multi-group particle swarm optimization algorithm. [PERSON] et al. [19] introduced the particle swarm optimization algorithm (PSO) and Gaussian factor into the pigeon heuristic optimization algorithm (PIO) to improve the elastic parameter inversion problem under the pre-stack amplitude variation with offset algorithm (AVO). [PERSON] et al. [20] proposed an intelligent inversion method of hydrogeological parameters based on the disturbance-inspired equilibrium optimizer (DIEO) to solve the problems of groundwater numerical simulation and sustainable utilization of water resources. The method was verified with three different types of cases, and the results showed that this method can accurately and quickly obtain hydrogeological parameters. [PERSON] et al. [21] and [PERSON] et al. [22] used intelligent parameter inversion analysis by means of depth network optimization algorithm and numerical analysis to realize multi-means coupling inversion to solve complex engineering problems.  
From the above analysis, it can be seen that the first two phases of the development of inversion analysis were limited by the level of field monitoring. The engineering problems were mainly solved by theoretical analysis and numerical inversion analysis methods. After entering the 1990s, the advent of the information age created opportunities for the transmission of various engineering information and the sharing of resources. The rapid development of computer vision has caused deep learning methods to penetrate into various fields. Consequently, in the latter two time nodes, inversion analysis has also entered the intelligent stage, forming an operational technology based on \"big data\" cloud system as the operation platform and intelligent inversion as the fundamental method to solve the problems of construction parameters and predict engineering disasters.